Wireless location using location estimators

ABSTRACT

A wireless location system is disclosed including one or more location centers for outputting locations of mobile stations (MS) for both local and global MS location requests via Internet communication between a distributed network of location centers. The system uses a plurality of MS locating technologies including those based on: two-way TOA and TDOA; pattern recognition; distributed antenna provisioning; GPS signals. Difficulties, such as multipath, poor location accuracy and poor coverage are alleviated via such technologies in combination with: (a) adapting and calibrating system performance according to environmental and geographical changes; (b) capturing location signal data for continual enhancement of an historical database; (c) evaluating MS locations via heuristics and constraints related to terrain, MS velocity and MS path extrapolation, and (d) adjusting likely MS locations. The system is useful for 911 emergency calls, tracking, routing, people and animal location including applications for confinement to and exclusion from certain areas.

RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.11/739,097 filed Apr. 24, 2007, which is a continuation of U.S.application Ser. No. 09/194,367 filed Nov. 24, 1998, now U.S. Pat. No.7,764,231, which is the National Stage of International Application No.PCT/US97/15892, filed Sep. 8, 1997, which claims the benefit of thefollowing three provisionals: U.S. Provisional Application No.60/056,590 filed Aug. 20, 1997; U.S. Provisional Application No.60/044,821 filed Apr. 25, 1997; and U.S. Provisional Application No.60/025,855 filed Sep. 9, 1996; each of the above identified applicationsare incorporated herein fully by reference.

FIELD OF THE INVENTION

The present invention is directed generally to a system and method forlocating people or objects, and in particular, to a system and methodfor locating a wireless mobile station using a plurality ofsimultaneously activated mobile station location estimators.

BACKGROUND

Introduction

Wireless communications systems are becoming increasingly importantworldwide. Wireless cellular telecommunications systems are rapidlyreplacing conventional wire-based telecommunications systems in manyapplications. Cellular radio telephone networks (“CRT”), and specializedmobile radio and mobile data radio networks are examples. The generalprinciples of wireless cellular telephony have been described variously,for example in U.S. Pat. No. 5,295,180 to Vendetti, et al, which isincorporated herein by reference.

There is great interest in using existing infrastructures for wirelesscommunication systems for locating people and/or objects in a costeffective manner. Such a capability would be invaluable in a variety ofsituations, especially in emergency or crime situations. Due to thesubstantial benefits of such a location system, several attempts havebeen made to design and implement such a system.

Systems have been proposed that rely upon signal strength andtrilateralization techniques to permit location include those disclosedin U.S. Pat. Nos. 4,818,998 and 4,908,629 to Apsell et al. (“the Apsellpatents”) and U.S. Pat. No. 4,891,650 to Sheffer (“the Sheffer patent”).However, these systems have drawbacks that include high expense in thatspecial purpose electronics are required. Furthermore, the systems aregenerally only effective in line-of-sight conditions, such as ruralsettings. Radio wave surface reflections, refractions and ground cluttercause significant distortion, in determining the location of a signalsource in most geographical areas that are more than sparsely populated.Moreover, these drawbacks are particularly exacerbated in dense urbancanyon (city) areas, where errors and/or conflicts in locationmeasurements can result in substantial inaccuracies.

Another example of a location system using time of arrival andtriangulation for location are satellite-based systems, such as themilitary and commercial versions of the Global Positioning Satellitesystem (“GPS”). GPS can provide accurate position determination (i.e.,about 100 meters error for the commercial version of GPS) from atime-based signal received simultaneously from at least threesatellites. A ground-based GPS receiver at or near the object to belocated determines the difference between the time at which eachsatellite transmits a time signal and the time at which the signal isreceived and, based on the time differentials, determines the object'slocation. However, the GPS is impractical in many applications. Thesignal power levels from the satellites are low and the GPS receiverrequires a clear, line-of-sight path to at least three satellites abovea horizon of about 60 degrees for effective operation. Accordingly,inclement weather conditions, such as clouds, terrain features, such ashills and trees, and buildings restrict the ability of the GPS receiverto determine its position. Furthermore, the initial GPS signal detectionprocess for a GPS receiver is relatively long (i.e., several minutes)for determining the receiver's position. Such delays are unacceptable inmany applications such as, for example, emergency response and vehicletracking.

Differential GPS, or DGPS systems offer correction schemes to accountfor time synchronization drift. Such correction schemes include thetransmission of correction signals over a two-way radio link orbroadcast via FM radio station subcarriers. These systems have beenfound to be awkward and have met with limited success.

Additionally, GPS-based location systems have been attempted in whichthe received GPS signals are transmitted to a central data center forperforming location calculations. Such systems have also met withlimited success. In brief, each of the various GPS embodiments have thesame fundamental problems of limited reception of the satellite signalsand added expense and complexity of the electronics required for aninexpensive location mobile station or handset for detecting andreceiving the GPS signals from the satellites.

Radio Propagation Background

The behavior of a mobile radio signal in the general environment isunique and complicated. Efforts to perform correlations between radiosignals and distance between a base station and a mobile station aresimilarly complex. Repeated attempts to solve this problem in the pasthave been met with only marginal success. Factors include terrainundulations, fixed and variable clutter, atmospheric conditions,internal radio characteristics of cellular and PCS systems, such asfrequencies, antenna configurations, modulation schemes, diversitymethods, and the physical geometries of direct, refracted and reflectedwaves between the base stations and the mobile. Noise, such as man-madeexternally sources (e.g., auto ignitions) and radio system co-channeland adjacent channel interference also affect radio reception andrelated performance measurements, such as the analogcarrier-to-interference ratio (C/I), or digital energy-per-bit/Noisedensity ratio (E_(b/No)) and are particular to various points in timeand space domains.

RF Propagation in Free Space

Before discussing real world correlations between signals and distance,it is useful to review the theoretical premise, that of radio energypath loss across a pure isotropic vacuum propagation channel, and itsdependencies within and among various communications channel types. FIG.1 illustrates a definition of channel types arising in communications:

Over the last forty years various mathematical expressions have beendeveloped to assist the radio mobile cell designer in establishing theproper balance between base station capital investment and the qualityof the radio link, typically using radio energy field-strength, usuallymeasured in microvolts/meter, or decibels. First consider Hata's singleray model. A simplified radio channel can be described as:G _(i) =L _(p) +F+L _(f) +L _(b) −G _(t) +G _(r)  (Equation 1)where

-   -   G_(i)=system gain in decibels    -   L_(p)=free space path loss in dB,    -   F=fade margin in dB,    -   L_(f)=transmission line loss from coaxials used to connect radio        to antenna, in dB,    -   L_(m)=miscellaneous losses such as minor antenna misalignment,        coaxial corrosion, increase in the receiver noise figure due to        aging, in dB,    -   L_(b)=branching loss due to filter and circulator used to        combine or split transmitter and receiver signals in a single        antenna    -   G_(t)=gain of transmitting antenna    -   G_(r)=gain of receiving antenna        Free space path loss¹ L_(p) as discussed in Mobile        Communications Design Fundamentals, William C. Y. Lee, 2nd, Ed        across the propagation channel is a function of distance d,        frequency        f (for f values <1 GHz, such as the 890-950 mHz cellular band):

$\begin{matrix}{\frac{P_{or}}{P_{t}} = \frac{1}{\left( {4\pi\;{dfc}} \right)^{2}}} & \left( {{equation}\mspace{14mu} 2} \right)\end{matrix}$where

P_(or)=received power in free space

P_(t)=transmitting power

c=speed of light,

The difference between two received signal powers in free space,

$\begin{matrix}{\Delta_{p} = {{(10){\log\left( \frac{p_{{or}\; 2}}{P_{{or}\; 1}} \right)}} = {(20){\log\left( \frac{d_{1}}{d_{2}} \right)}({dB})}}} & \left( {{equation}\mspace{14mu} 3} \right)\end{matrix}$indicates that the free propagation path loss is 20 dB per decade.Frequencies between 1 GHz and 2 GHz experience increased values in theexponent, ranging from 2 to 4, or 20 to 40 dB/decade, which would bepredicted for the new PCS 1.8-1.9 GHz band.

This suggests that the free propagation path loss is 20 dB per decade.However, frequencies between 1 GHz and 2 GHz experience increased valuesin the exponent, ranging from 2 to 4, or 20 to 40 dB/decade, which wouldbe predicted for the new PCS 1.8-1.9 GHz band. One consequence from alocation perspective is that the effective range of values for higherexponents is an increased at higher frequencies, thus providing improvedgranularity of ranging correlation.

Environmental Clutter and RF Propagation Effects

Actual data collected in real-world environments uncovered hugevariations with respect to the free space path loss equation, givingrise to the creation of many empirical formulas for radio signalcoverage prediction. Clutter, either fixed or stationary in geometricrelation to the propagation of the radio signals, causes a shadow effectof blocking that perturbs the free space loss effect. Perhaps the bestknown model set that characterizes the average path loss is Hata's,“Empirical Formula for Propagation Loss in Land Mobile Radio”, M. Hata,IEEE Transactions VT-29, pp. 317-325, August 1980, three pathlossmodels, based on Okumura's measurements in and around Tokyo, “FieldStrength and its Variability in VHF and UHF Land Mobile Service”, Y.Okumura, et al, Review of the Electrical Communications laboratory, Vol16, pp 825-873, September-October 1968.

The typical urban Hata model for L_(p) was defined as L_(p)=L_(hu):L _(Hu)=69.55+26.16 log(f)−13.82 log(h _(BS))−a(h _(MS))+((44.9−6.55log(H _(BS))log(d)[dB])  (Equation 4)where

L_(Hu)=path loss, Hata urban

h_(BS)=base station antenna height

h_(MS)=mobile station antenna height

d=distance BS-MS in km

a(h_(MS)) is a correction factor for small and medium sized cities,found to be:1 log(f−0.7)h _(MS)−1.56 log(f−0.8)=a(h _(MS))  (Equation 5)For large cities the correction factor was found to be:a(h _(MS))=3.2[log 11.75h _(MS)]²−4.97  (Equation 6)assuming f is equal to or greater than 400 mHz.The typical suburban model correction was found to be:

$\begin{matrix}{L_{H_{suburban}} = {L_{Hu} - {2\left\lbrack {\log\left( \frac{f}{28} \right)}^{2} \right\rbrack} - {5.4\lbrack{dB}\rbrack}}} & \left( {{Equation}\mspace{14mu} 7} \right)\end{matrix}$The typical rural model modified the urban formula differently, as seenbelow:L _(Hrural) =L _(Hu)−4.78(log f)²+18.33 log f−40.94[dB]  (Equation 8)

Although the Hata model was found to be useful for generalized RF waveprediction in frequencies under 1 GHz in certain suburban and ruralsettings, as either the frequency and/or clutter increased,predictability decreased. In current practice, however, fieldtechnicians often have to make a guess for dense urban an suburban areas(applying whatever model seems best), then installing a base stationsand begin taking manual measurements. Coverage problems can take up to ayear to resolve.

Relating Received Signal Strength to Location

Having previously established a relationship between d and P_(or),reference equation 2 above: d represents the distance between the mobilestation (MS) and the base station (BS); P_(or) represents the receivedpower in free space) for a given set of unchanging environmentalconditions, it may be possible to dynamically measure P_(or) and thendetermine d.

In 1991, U.S. Pat. No. 5,055,851 to Sheffer taught that if three or morerelationships have been established in a triangular space of three ormore base stations (BSs) with a location database constructed havingdata related to possible mobile station (MS) locations, then arculationcalculations may be performed, which use three distinct P_(or)measurements to determine an X, Y, two dimensional location, which canthen be projected onto an area map. The triangulation calculation isbased on the fact that the approximate distance of the mobile station(MS) from any base station (BS) cell can be calculated based on thereceived signal strength. Sheffer acknowledges that terrain variationsaffect accuracy, although as noted above, Sheffer's disclosure does notaccount for a sufficient number of variables, such as fixed and variablelocation shadow fading, which are typical in dense urban areas withmoving traffic.

Most field research before about 1988 has focused on characterizing(with the objective of RF coverage prediction) the RF propagationchannel (i.e., electromagnetic radio waves) using a single-ray model,although standard fit errors in regressions proved dismal (e.g., 40-80dB). Later, multi-ray models were proposed, and much later, certainbehaviors were studied with radio and digital channels. In 1981, Voglerproposed that radio waves at higher frequencies could be modeled usingoptics principles. In 1988 Walfisch and Bertoni applied optical methodsto develop a two-ray model, which when compared to certain highlyspecific, controlled field data, provided extremely good regression fitstandard errors of within 1.2 dB.

In the Bertoni two ray model it was assumed that most cities wouldconsist of a core of high-rise buildings surrounded by a much largerarea having buildings of uniform height spread over regions comprisingmany square blocks, with street grids organizing buildings into rowsthat are nearly parallel. Rays penetrating buildings then emanatingoutside a building were neglected. FIG. 2 provides a basis for thevariables.

After a lengthy analysis it was concluded that path loss was a functionof three factors: (1) the path loss between antennas in free space; (2)the reduction of rooftop wave fields due to settling; and (3) the effectof diffraction of the rooftop fields down to ground level. The last twofactors were summarily termed L_(ex), given by:

$\begin{matrix}{L_{ex} = {57.1 + A + {\log(f)} + R - \left( {\left( {18\mspace{14mu}{\log(H)}} \right) - {18\mspace{14mu}{\log\left\lbrack {1 - \frac{R^{2}}{17\; H}} \right\rbrack}}} \right.}} & \left( {{Equation}\mspace{14mu} 9} \right)\end{matrix}$The influence of building geometry is contained in A:

$\begin{matrix}{A = {{5\;{\log\left\lbrack \left( \frac{d}{2} \right)^{2} \right\rbrack}} - {9\;\log\; d} + {20\log\left\{ {\tan\left\lbrack {2\left( {h - H_{MS}} \right)} \right\rbrack}^{- 1} \right\}}}} & \left( {{Equation}\mspace{14mu} 10} \right)\end{matrix}$

However, a substantial difficulty with the two-ray model in practice isthat it requires a substantial amount of data regarding buildingdimensions, geometries, street widths, antenna gain characteristics forevery possible ray path, etc. Additionally, it requires an inordinateamount of computational resources and such a model is not easily updatedor maintained.

Unfortunately, in practice clutter geometries and building heights arerandom. Moreover, data of sufficient detail has been extremely difficultto acquire, and regression standard fit errors are poor; i.e., in thegeneral case, these errors were found to be 40-60 dB. Thus the two-raymodel approach, although sometimes providing an improvement over singleray techniques, still did not predict RF signal characteristics in thegeneral case to level of accuracy desired (<10 dB).

Work by Greenstein has since developed from the perspective ofmeasurement-based regression models, as opposed to the previous approachof predicting-first, then performing measurement comparisons. Apparentlyyielding to the fact that low-power, low antenna (e.g., 12-25 feet aboveground) height PCS microcell coverage was insufficient in urbanbuildings, Greenstein, et al, authored “Performance Evaluations forUrban Line-of-sight Microcells Using a Multi-ray Propagation Model”, inIEEE Globecom Proceedings, 12/91. This paper proposed the idea offormulating regressions based on field measurements using small PCSmicrocells in a lineal microcell geometry (i.e., geometries in whichthere is always a line-of-sight (LOS) path between a subscriber's mobileand its current microsite).

Additionally, Greenstein studied the communication channels variableBit-Error-Rate (BER) in a spatial domain, which was a departure fromprevious research that limited field measurements to the RF propagationchannel signal strength alone. However, Greenstein based his finding ontwo suspicious assumptions: 1) he assumed that distance correlationestimates were identical for uplink and downlink transmission paths; and2) modulation techniques would be transparent in terms of improveddistance correlation conclusions. Although some data held verycorrelations, other data and environments produced poor results.Accordingly, his results appear unreliable for use in general locationcontext.

In 1993 Greenstein, et al, authored “A Measurement-Based Model forPredicting Coverage Areas of Urban Microcells”, in the IEEE Journal OnSelected Areas in Communications, Vol. 11, No. 7, 9/93. Greensteinreported a generic measurement-based model of RF attenuation in terms ofconstant-value contours surrounding a given low-power, low antennamicrocell environment in a dense, rectilinear neighborhood, such as NewYork City. However, these contours were for the cellular frequency band.In this case, LOS and non-LOS clutter were considered for a givenmicrocell site. A result of this analysis was that RF propagation losses(or attenuations), when cell antenna heights were relatively low,provided attenuation contours resembling a spline plane curve depictedas an asteroid, aligned with major street grid patterns. Further,Greenstein found that convex diamond-shaped RF propagation loss contourswere a common occurrence in field measurements in a rectilinear urbanarea. The special plane curve asteroid is represented by the formulax^(2/3)+y^(2/3)=r^(2/3). However, these results alone have not beensufficiently robust and general to accurately locate an MS, due to thevariable nature of urban clutter spatial arrangements.

At Telesis Technology in 1994 Howard Xia, et al, authored “MicrocellularPropagation Characteristics for Personal Communications in Urban andSuburban Environments”, in IEEE Transactions of Vehicular Technology,Vol. 43, No. 3, 8/94, which performed measurements specifically in thePCS 1.8 to 1.9 GHz frequency band. Xia found corresponding but morevariable outcome results in San Francisco, Oakland (urban) and theSunset and Mission Districts (suburban).

Summary of Factors Affecting RF Propagation

The physical radio propagation channel perturbs signal strength,frequency (causing rate changes, phase delay, signal to noise ratios(e.g., C/I for the analog case, or E_(b/No), RF energy per bit, overaverage noise density ratio for the digital case) and Doppler-shift.Signal strength is usually characterized by:

-   -   Free Space Path Loss (L_(p))    -   Slow fading loss or margin (L_(slow))    -   Fast fading loss or margin (L_(fast))

Loss due to slow fading includes shadowing due to clutter blockage(sometimes included in Lp). Fast fading is composed of multipathreflections which cause: 1) delay spread; 2) random phase shift orRayleigh fading; and 3) random frequency modulation due to differentDoppler shifts on different paths.

Summing the path loss and the two fading margin loss components from theabove yields a total path loss of:L _(total) =L _(p) +L _(slow) +L _(fast)Referring to FIG. 3, the figure illustrates key components of a typicalcellular and PCS power budget design process. The cell designerincreases the transmitted power P_(TX) by the shadow fading marginL_(slow) which is usually chosen to be within the 1-2 percentile of theslow fading probability density function (PDF) to minimize theprobability of unsatisfactorily low received power level P_(RX) at thereceiver. The P_(RX) level must have enough signal to noise energy level(e.g., 10 dB) to overcome the receiver's internal noise level (e.g.,−118 dBm in the case of cellular 0.9 GHz), for a minimum voice qualitystandard. Thus in the example P_(RX) must never be below −108 dBm, inorder to maintain the quality standard.

Additionally the short term fast signal fading due to multipathpropagation is taken into account by deploying fast fading marginL_(fast), which is typically also chosen to be a few percentiles of thefast fading distribution. The 1 to 2 percentiles compliment othernetwork blockage guidelines. For example the cell base station trafficloading capacity and network transport facilities are usually designedfor a 1-2 percentile blockage factor as well. However, in the worst-casescenario both fading margins are simultaneously exceeded, thus causing afading margin overload.

In Roy, Steele's, text, Mobile Radio Communications, IEEE Press, 1992,estimates for a GSM system operating in the 1.8 GHz band with atransmitter antenna height of 6.4 m and an MS receiver antenna height of2 m, and assumptions regarding total path loss, transmitter power wouldbe calculated as follows:

TABLE 1 GSM Power Budget Example Parameter dBm value Will requireL_(slow) 14 L_(fast) 7 Ll_(path) 110 Min. RX pwr required −104 TXpwr =27 dBmSteele's sample size in a specific urban London area of 80,000 LOSmeasurements and data reduction found a slow fading variance ofσ=7dBassuming log normal slow fading PDF and allowing for a 1.4% slow fadingmargin overload, thusL _(slow)=2σ=14dBThe fast fading margin was determined to be:L _(fast)=7dB

In contrast, Xia's measurements in urban and suburban California at 1.8GHz uncovered flat-land shadow fades on the order of 25-30 dB when themobile station (MS) receiver was traveling from LOS to non-LOSgeometries. In hilly terrain fades of +5 to −50 dB were experienced.Thus it is evident that attempts to correlate signal strength with MSranging distance suggest that error ranges could not be expected toimprove below 14 dB, with a high side of 25 to 50 dB. Based on 20 to 40dB per decade, Corresponding error ranges for the distance variablewould then be on the order of 900 feet to several thousand feet,depending upon the particular environmental topology and the transmitterand receiver geometries.

Summary

It is an objective of the present disclosure to provide a system andmethod for to wireless telecommunication systems for accurately locatingpeople and/or objects in a cost effective manner. Additionally, it is anobjective of the present disclosure to provide such locationcapabilities using the measurements from wireless signals communicatedbetween mobile stations and a network of base stations, wherein the samecommunication standard or protocol is utilized for location as is usedby the network of base stations for providing wireless communicationswith mobile stations for other purposes such as voice communicationand/or visual communication (such as text paging, graphical or videocommunications). Related objectives for the present disclosure includeproviding a system and method that:

(1.1) can be readily incorporated into existing commercial wirelesstelephony systems with few, if any, modifications of a typical telephonywireless infrastructure;

(1.2) can use the native electronics of typical commercially available,or likely to be available, telephony wireless mobile stations (e.g.,handsets) as location devices;

(1.3) can be used for effectively locating people and/or objects whereinthere are few (if any) line-of-sight wireless receivers for receivinglocation signals from a mobile station (herein also denoted MS);

(1.4) can be used not only for decreasing location determiningdifficulties due to multipath phenomena but in fact uses such multipathfor providing more accurate location estimates;

(1.5) can be used for integrating a wide variety of location techniquesin a straight-forward manner; and

(1.6) can substantially automatically adapt and/or (re)train and/or(re)calibrate itself according to changes in the environment and/orterrain of a geographical area where the present disclosure is utilized;and

(1.7) can utilize a plurality of wireless location estimators based ondifferent wireless location technologies (e.g., GPS location techniques,terrestrial base station signal timing techniques for triangulationand/or trilateration, wireless signal angle of arrival locationtechniques, techniques for determining a wireless location within abuilding, techniques for determining a mobile station location usingwireless location data collected from the wireless coverage area for,e.g., location techniques using base station signal coverage areas,signal pattern matching location techniques and/or stochastictechniques), wherein each such estimator may be activated independentlyof one another, whenever suitable data is provided thereto and/orcertain conditions, e.g., specific to the estimator are met;(1.8) can provide a common interface module from which a plurality ofthe location estimators can be activated and/or provided with input;(1.9) provides resulting mobile station location estimates to locationrequesting applications (e.g., for 911 emergency, the fire or policedepartments, taxi services, vehicle location, etc.) via an outputgateway, wherein this gateway:

-   -   (a) routes the mobile station location estimates to the        appropriate location application(s) via a communications network        such as a wireless network, a public switched telephone network,        a short messaging service (SMS), and the Internet,    -   (b) determines the location granularity and representation        desired by each location application requesting a location of a        mobile station, and/or    -   (c) enhances the received location estimates by, e.g.,        performing additional processing such as “snap to street”        functions for mobile stations known to reside in a vehicle.

Yet another objective is to provide a low cost location system andmethod, adaptable to wireless telephony systems, for usingsimultaneously a plurality of location techniques for synergisticallyincreasing MS location accuracy and consistency.

It is yet another objective of the present disclosure that at least someof the following MS location techniques can be utilized by variousembodiments of the present disclosure:

(2.1) time-of-arrival wireless signal processing techniques;

(2.2) time-difference-of-arrival wireless signal processing techniques;

(2.3) adaptive wireless signal processing techniques having, forexample, learning capabilities and including, for instance, artificialneural net and genetic algorithm processing;

(2.4) signal processing techniques for matching MS location signals withwireless signal characteristics of known areas;

(2.5) conflict resolution techniques for resolving conflicts inhypotheses for MS location estimates;

(2.6) enhancement of MS location estimates through the use of bothheuristics and historical data associating MS wireless signalcharacteristics with known locations and/or environmental conditions.

Yet another objective is to provide location estimates in terms of timevectors, which can be used to establish motion, speed, and anextrapolated next location in cases where the MS signal subsequentlybecomes unavailable.

DEFINITIONS

The following definitions are provided for convenience. In general, thedefinitions here are also provided elsewhere in this document as well.

(3.1) The term “wireless” herein is, in general, an abbreviation for“digital wireless”, and in particular, “wireless” refers to digitalradio signaling using one of standard the wireless protocols such asCDMA, NAMPS, AMPS, TDMA, GSM as one skilled in the art will understand.(3.2) As used herein, the term “mobile station” (equivalently, MS)refers to a wireless device that is at least a transmitting device, andin most cases is also a wireless receiving device, such as a portableradio telephony handset. Note that in some contexts herein instead of,or in addition to, the terms “mobile station” or “MS”, the followingterms may be synonymously also used: “personal station” (PS), and“location unit” (LU). In general, these terms may be consideredsynonymous. However, the later two terms may be used when referring toreduced functionality communication devices in comparison to a typicaldigital wireless mobile telephone.(3.3) The term, “infrastructure”, denotes the network of telephonycommunication services, and more particularly, that portion of such anetwork that receives and processes wireless communications withwireless mobile stations. In particular, this infrastructure includestelephony wireless base stations (BS) such as those for radio mobilecommunication systems based on CDMA, AMPS, NAMPS, TDMA, and GSM whereinthe base stations provide a network of cooperative communicationchannels with an air interface with the MS, and a conventionaltelecommunications interface with a Mobile Switch Center (MSC). Thus, anMS user within an area serviced by the base stations may be providedwith wireless communication throughout the area by user transparentcommunication transfers (i.e., “handoffs”) between the user's MS andthese base stations in order to maintain effective telephony service.The mobile switch center (MSC) provides communications and controlconnectivity among base stations and the public telephone network 124(FIG. 4).(3.4) The phrase, “composite wireless signal characteristic values”denotes the result of aggregating and filtering a collection ofmeasurements of wireless signal samples, wherein these samples areobtained from the wireless communication between an MS to be located andthe base station infrastructure (e.g., a plurality of networked basestations). However, other phrases are also used herein to denote thiscollection of derived characteristic values depending on the context andthe likely orientation of the reader. For example, when viewing thesevalues from a wireless signal processing perspective of radioengineering, as in the descriptions of the subsequent DetailedDescription sections concerned with the aspects for receiving MS signalmeasurements from the base station infrastructure, the phrase typicallyused is: “RF signal measurements”. Alternatively, from a data processingperspective, the phrases: “location signature cluster” and “locationsignal data” are used to describe signal characteristic values betweenthe MS and the plurality of infrastructure base stations substantiallysimultaneously detecting MS transmissions. Moreover, since the locationcommunications between an MS and the base station infrastructuretypically include simultaneous communications with more than one basestation, a related useful notion is that of a “location signature” whichis the composite wireless signal characteristic values for signalsamples between an MS to be located and a single base station. Also, insome contexts, the phrases: “signal characteristic values” or “signalcharacteristic data” are used when either or both a locationsignature(s) and/or a location signature cluster(s) are intended.Summary

The present disclosure relates to a wireless mobile station locationsystem and method for performing wireless location of mobile stations.In particular, the present disclosure is directed to hybriding,combining or otherwise using wireless location determining techniquesthat estimate a geographical location(s) of each of one or more mobilestations using, for each such mobile station, wireless signalcharacteristics from one or more of: (i) a signal strength, a time ofarrival, or time difference of arrival of wireless signals communicatedbetween the mobile station, and terrestrial base stations, and/or (ii)wireless signal direction information of wireless signals communicatedbetween the mobile station, and terrestrial base stations. Inparticular, depending on the wireless signals obtained for determining ageolocation of a mobile station, wireless signal characteristics: from(i) only above may be used, or from (ii) only above may be used, or fromboth (i) and (ii) above may used.

In one embodiment, the present disclosure is directed to what is knownas “network centric” wireless location techniques between one of themobile stations and a network of terrestrial (Earth supported) basestations.

Regarding a wireless location gateway, this term refers to acommunications network node whereat a plurality of location requests arereceived for locating various mobile stations from various sources(e.g., for E911 requests, for stolen vehicle location, for tracking ofvehicles traveling cross country, etc.), and for each such request andthe corresponding mobile station to be located, this node: (a) activatesone or more wireless location estimators for locating the mobilestation, (b) receives one or more location estimates of the mobilestation from the location estimators, and (c) transmits a resultinglocation estimate(s) to, e.g., an application which made the request.Moreover, such a gateway typically will likely activate locationestimators according to the particulars of each individual wirelesslocation request, e.g., the availability of input data needed byparticular location estimators. Additionally, such a gateway willtypically have sufficiently well defined uniform interfaces so that suchlocation estimators can be added and/or deleted to, e.g., providedifferent location estimators for performing wireless location differentcoverage areas.

The present disclosure encompasses such wireless location gateways.Thus, for locating an identified mobile station, the location gatewayembodiments of the present disclosure may activate one or more of aplurality of location estimators depending on, e.g., (a) theavailability of particular types of wireless location data for locatingthe mobile station, and (b) the location estimators accessible by thelocation gateway. Moreover, a plurality of location estimators may beactivated for locating the mobile station in a single location, ordifferent ones of such location estimators may be activated to locatethe mobile station at different locations. Moreover, the locationgateway of the present disclosure may have incorporated therein one ormore of the location estimators, and/or may access geographicallydistributed location estimators via requests through a communicationsnetwork such as the Internet.

In particular, the location gateway of the present disclosure mayaccess, in various instances of locating mobile stations, variouslocation estimators that utilize one or more of the following wirelesslocation techniques:

-   -   (a) A GPS location technique such as, e.g., one of the GPS        location techniques as described in the Background section        hereinabove;    -   (b) A technique for computing a mobile station location that is        dependent upon geographical offsets of the mobile station from        one or more terrestrial transceivers (e.g., base stations of a        commercial radio service provider). Such offsets may be        determined from signal time delays between such transceivers and        the mobile station, such as by time of arrival (TOA) and/or time        difference of arrival (TDOA) techniques as is discussed further        hereinbelow. Moreover, such offsets may be determined using both        the forward and reverse wireless signal timing measurements of        transmissions between the mobile station and such terrestrial        transceivers. Additionally, such offsets may be directional        offsets, wherein a direction is determined from such a        transceiver to the mobile station;    -   (c) Various wireless signal pattern matching, associative,        and/or stochastic techniques for performing comparisons and/or        using a learned association between:        -   (i) characteristics of wireless signals communicated between            a mobile station to be located and a network of wireless            transceivers (e.g., base stations), and        -   (ii) previously obtained sets of characteristics of wireless            signals (from each of a plurality of locations), wherein            each set was communicated, e.g., between a network of            transceivers (e.g., the fixed location base stations of a            commercial radio service provider), and, some one of the            mobile stations available for communicating with the            network;    -   (d) Indoor location techniques using a distributed antenna        system;    -   (e) Techniques for locating a mobile station, wherein, e.g.,        wireless coverage areas of individual fixed location        transceivers (e.g., fixed location base stations) are utilized        for determining the mobile station's location (e.g.,        intersecting such coverage areas for determining a location);    -   (f) Location techniques that use communications from low power,        low functionality base stations (denoted “location base        stations”); and    -   (g) Any other location techniques that may be deemed worthwhile        to incorporate into an embodiment of the present disclosure.

Accordingly, some embodiments of the present disclosure may be viewed asplatforms for integrating wireless location techniques in that wirelesslocation computational models (denoted “first order models” or “FOMs”hereinbelow) may be added and/or deleted from such embodiments of theinvention without changing the interface to further downstreamprocesses. That is, one aspect of the invention is the specification ofa common data interface between such computational models and subsequentlocation processing such as processes for combining of locationestimates, tracking mobile stations, and/or outputting locationestimates to location requesting applications.

Moreover, it should be noted that the present disclosure alsoencompasses various hybrid approaches to wireless location, whereinvarious combinations of two or more of the location techniques (a)through (g) immediately above may be used in locating a mobile stationat substantially a single location. Thus, location information may beobtained from a plurality of the above location techniques for locatinga mobile station, and the output from such techniques can besynergistically used for deriving therefrom an enhanced locationestimate of the mobile station.

It is a further aspect of the present disclosure that it may be used towirelessly locate a mobile station: (a) from which a 911 emergency callis performed, (b) for tracking a mobile station (e.g., a truck travelingacross country), (c) for routing a mobile station, and (d) locatingpeople and/or animals, including applications for confinement to (and/orexclusion from) certain areas.

It is a further aspect of the present disclosure that it such a wirelessmobile station location system may be decomposed into: (i) a first lowlevel wireless signal processing subsystem for receiving, organizing andconditioning low level wireless signal measurements from a network ofbase stations cooperatively linked for providing wireless communicationswith mobile stations (MSs); and (ii) a second high level signalprocessing subsystem for performing high level data processing forproviding most likelihood location estimates for mobile stations.

Thus, the present disclosure may be considered as a novel signalprocessor that includes at least the functionality for the high levelsignal processing subsystem mentioned hereinabove. Accordingly, assumingan appropriate ensemble of wireless signal measurements characterizingthe wireless signal communications between a particular MS and anetworked wireless base station infrastructure have been received andappropriately filtered of noise and transitory values (such as by anembodiment of the low level signal processing subsystem disclosed in acopending PCT patent application PCT/US97/15933 titled, “WirelessLocation Using A Plurality of Commercial Network Infrastructures,” by F.W. LeBlanc et. al., filed Sep. 8, 1997 from which U.S. Pat. No.6,236,365, filed Jul. 8, 1999 is the U.S. national counterpart, thesetwo references being herein fully incorporated by reference), thepresent disclosure uses the output from such a low level signalprocessing system for determining a most likely location estimate of anMS.

That is, once the following steps are appropriately performed (e.g., bythe LeBlanc U.S. Pat. No. 6,236,365):

-   -   (4.1) receiving signal data measurements corresponding to        wireless communications between an MS to be located (herein also        denoted the “target MS”) and a wireless telephony        infrastructure;—    -   (4.2) organizing and processing the signal data measurements        received from a given target MS and surrounding BSs so that        composite wireless signal characteristic values may be obtained        from which target MS location estimates may be subsequently        derived. In particular, the signal data measurements are        ensembles of samples from the wireless signals received from the        target MS by the base station infrastructure, wherein these        samples are subsequently filtered using analog and digital        spectral filtering.        The present disclosure accomplishes the objectives mentioned        above by the following steps:    -   (4.3) providing the composite signal characteristic values to        one or more MS location hypothesizing computational models (also        denoted herein as “first order models” and also “location        estimating models”), wherein each such model subsequently        determines one or more initial estimates of the location of the        target MS based on, for example, the signal processing        techniques 2.1 through 2.3 above. Moreover, each of the models        output MS location estimates having substantially identical data        structures (each such data structure denoted a “location        hypothesis”). Additionally, each location hypothesis may also        includes a confidence value indicating the likelihood or        probability that the target MS whose location is desired resides        in a corresponding location estimate for the target MS;    -   (4.4) adjusting or modifying location hypotheses output by the        models according to, for example, 2.4 through 2.6 above so that        the adjusted location hypotheses provide better target MS        location estimates. In particular, such adjustments are        performed on both the target MS location estimates of the        location hypotheses as well as their corresponding confidences;        and    -   (4.4) subsequently computing a “most likely” target MS location        estimate for outputting to a location requesting application        such as 911 emergency, the fire or police departments, taxi        services, etc. Note that in computing the most likely target MS        location estimate a plurality of location hypotheses may be        taken into account. In fact, it is an important aspect of the        present disclosure that the most likely MS location estimate is        determined by computationally forming a composite MS location        estimate utilizing such a plurality of location hypotheses so        that, for example, location estimate similarities between        location hypotheses can be effectively utilized.

Referring now to (4.3) above, the filtered and aggregated wirelesssignal characteristic values are provided to a number of locationhypothesizing models (denoted First Order Models, or FOMs), each ofwhich yields a location estimate or location hypothesis related to thelocation of the target MS. In particular, there are location hypothesesfor both providing estimates of where the target MS likely to be andwhere the target MS is not likely to be. Moreover, it is an aspect ofthe present disclosure that confidence values of the location hypothesesare provided as a continuous range of real numbers from, e.g., −1 to 1,wherein the most unlikely areas for locating the target MS are given aconfidence value of −1, and the most likely areas for locating thetarget MS are given a confidence value of 1. That is, confidence valuesthat are larger indicate a higher likelihood that the target MS is inthe corresponding MS estimated area, wherein 1 indicates that the targetMS is absolutely NOT in the estimated area, 0 indicates a substantiallyneutral or unknown likelihood of the target MS being in thecorresponding estimated area, and 1 indicates that the target MS isabsolutely within the corresponding estimated area.

Referring to (4.4) above, it is an aspect of the present disclosure toprovide location hypothesis enhancing and evaluation techniques that canadjust target MS location estimates according to historical MS locationdata and/or adjust the confidence values of location hypothesesaccording to how consistent the corresponding target MS locationestimate is: (a) with historical MS signal characteristic values, (b)with various physical constraints, and (c) with various heuristics. Inparticular, the following capabilities are provided by the presentdisclosure:

-   -   (5.1) a capability for enhancing the accuracy of an initial        location hypothesis, H, generated by a first order model,        FOM_(H), by using H as, essentially, a query or index into an        historical data base (denoted herein as the location signature        data base), wherein this data base includes: (a) a plurality of        previously obtained location signature clusters (i.e., composite        wireless signal characteristic values) such that for each such        cluster there is an associated actual or verified MS locations        where an MS communicated with the base station infrastructure        for locating the MS, and (b) previous MS location hypothesis        estimates from FOM_(H) derived from each of the location        signature clusters stored according to (a);    -   (5.2) a capability for analyzing composite signal characteristic        values of wireless communications between the target MS and the        base station infrastructure, wherein such values are compared        with composite signal characteristics values of known MS        locations (these latter values being archived in the location        signature data base). In one instance, the composite signal        characteristic values used to generate various location        hypotheses for the target MS are compared against wireless        signal data of known MS locations stored in the location        signature data base for determining the reliability of the        location hypothesizing models for particular geographic areas        and/or environmental conditions;    -   (5.3) a capability for reasoning about the likeliness of a        location hypothesis wherein this reasoning capability uses        heuristics and constraints based on physics and physical        properties of the location geography;    -   (5.4) an hypothesis generating capability for generating new        location hypotheses from previous hypotheses.

As also mentioned above in (2.3), the present disclosure utilizesadaptive signal processing techniques. One particularly importantutilization of such techniques includes the automatic tuning of theembodiments disclosed herein so that, e.g., such tuning can be appliedto adjusting the values of location processing system parameters thataffect the processing performed by the embodiments disclosed herein. Forexample, such system parameters as those used for determining the sizeof a geographical area to be specified when retrieving location signaldata of known MS locations from the historical (location signature) database can substantially affect the location processing. In particular, asystem parameter specifying a minimum size for such a geographical areamay, if too large, cause unnecessary inaccuracies in locating an MS.Accordingly, to accomplish a tuning of such system parameters, anadaptation engine is included in the for automatically adjusting ortuning parameters used by the embodiments disclosed herein. Note that inone embodiment, the adaptation engine is based on genetic algorithmtechniques.

A novel aspect of the present disclosure relies on the discovery that inmany areas where MS location services are desired, the wireless signalmeasurements obtained from communications between the target MS and thebase station infrastructure are extensive enough to provide sufficientlyunique or peculiar values so that the pattern of values alone mayidentify the location of the target MS. Further, assuming a sufficientamount of such location identifying pattern information is captured inthe composite wireless signal characteristic values for a target MS, andthat there is a technique for matching such wireless signal patterns togeographical locations, then a FOM based on this technique may generatea reasonably accurate target MS location estimate. Moreover, if theembodiments disclosed herein (e.g., the location signature data base)has captured sufficient wireless signal data from locationcommunications between MSs and the base station infrastructure whereinthe locations of the MSs are also verified and captured, then thiscaptured data (e.g., location signatures) can be used to train orcalibrate such models to associate the location of a target MS with thedistinctive signal characteristics between the target MS and one or morebase stations. Accordingly, a system or method according to the presentdisclosure may include one or more FOMs that may be generally denoted asclassification models wherein such FOMs are trained or calibrated toassociate particular composite wireless signal characteristic valueswith a geographical location where a target MS could likely generate thewireless signal samples from which the composite wireless signalcharacteristic values are derived. Further, a system or method accordingto the present disclosure may include the capability for training(calibrating) and retraining (recalibrating) such classification FOMs toautomatically maintain the accuracy of these models even thoughsubstantial changes to the radio coverage area may occur, such as theconstruction of a new high rise building or seasonal variations (due to,for example, foliage variations).

Note that such classification FOMs that are trained or calibrated toidentify target MS locations by the wireless signal patterns producedconstitute a particularly novel aspect of the present disclosure. It iswell known in the wireless telephony art that the phenomenon of signalmultipath and shadow fading renders most analytical locationcomputational techniques such as time-of-arrival (TOA) ortime-difference-of-arrival (TDOA) substantially useless in urban areasand particularly in dense urban areas. However, this same multipathphenomenon also may produce substantially distinct or peculiar signalmeasurement patterns, wherein such a pattern coincides with a relativelysmall geographical area. Thus, a system or method according to thepresent disclosure may include utilizes multipath as an advantage forincreasing accuracy where for previous location systems multipath hasbeen a source of substantial inaccuracies. Moreover, it is worthwhile tonote that the utilization of classification FOMs in high multipathenvironments is especially advantageous in that high multipathenvironments are typically densely populated. Thus, since suchenvironments are also capable of yielding a greater density of MSlocation signal data from MSs whose actual locations can be obtained,there can be a substantial amount of training or calibration datacaptured by a system or method according to the present disclosure mayinclude for training or calibrating such classification FOMs and forprogressively improving the MS location accuracy of such models.Moreover, since it is also a related aspect of a system or methodaccording to the present disclosure may include to include a pluralitystationary, low cost, low power “location detection base stations”(LBS), each having both restricted range MS detection capabilities and abuilt-in MS, a grid of such LBSs can be utilized for providing locationsignal data (from the built-in MS) for (re)training or (re)calibratingsuch classification FOMs.

In one embodiment of a system and/or method according to the presentdisclosure, one or more classification FOMs may each include a learningmodule such as an artificial neural network (ANN) for associating targetMS location signal data with a target MS location estimate.Additionally, one or more classification FOMs may be statisticalprediction models based on such statistical techniques as, for example,principle decomposition, partial least squares, or other regressiontechniques.

It is a further aspect of the present disclosure that the personalcommunication system (PCS) infrastructures currently being developed bytelecommunication providers offer an appropriate localizedinfrastructure base upon which to build various personal locationsystems (PLS) employing a system and/or method according to the presentdisclosure and/or utilizing the techniques disclosed herein. Inparticular, a system and/or method according to the present disclosureis especially suitable for the location of people and/or objects usingcode division multiple access (CDMA) wireless infrastructures, althoughother wireless infrastructures, such as, time division multiple access(TDMA) infrastructures and GSM are also contemplated. Note that CDMApersonal communications systems are described in the TelephoneIndustries Association standard IS-95, for frequencies below 1 GHz, andin the Wideband Spread-Spectrum Digital Cellular System Dual-Mode MobileStation-Base Station Compatibility Standard, for frequencies in the1.8-1.9 GHz frequency bands, both of which are incorporated herein byreference. Furthermore, CDMA general principles have also beendescribed, for example, in U.S. Pat. No. 5,109,390, to Gilhousen, et al,filed Nov. 7, 1989, and CDMA Network Engineering Handbook by Qualcomm,Inc., each of which is also incorporated herein by reference.

Notwithstanding the above mentioned CDMA references, a briefintroduction of CDMA is given here. Briefly, CDMA is an electromagneticsignal modulation and multiple access scheme based on spread spectrumcommunication. Each CDMA signal corresponds to an unambiguouspseudorandom binary sequence for modulating the carrier signalthroughout a predetermined spectrum of bandwidth frequencies.Transmissions of individual CDMA signals are selected by correlationprocessing of a pseudonoise waveform. In particular, the CDMA signalsare separately detected in a receiver by using a correlator, whichaccepts only signal energy from the selected binary sequence anddespreads its spectrum. Thus, when a first CDMA signal is transmitted,the transmissions of unrelated CDMA signals correspond to pseudorandomsequences that do not match the first signal. Therefore, these othersignals contribute only to the noise and represent a self-interferencegenerated by the personal communications system.

As mentioned in the discussion of classification FOMs above, a systemand/or method according to the present disclosure can substantiallyautomatically retrain and/or recalibrate itself to compensate forvariations in wireless signal characteristics (e.g., multipath) due toenvironmental and/or topographic changes to a geographic area servicedby a system and/or method according to the present disclosure. Forexample, in one embodiment of a system and/or method according to thepresent disclosure there may be low cost, low power base stations,denoted location base stations (LBS) above, providing, for example, CDMApilot channels to a very limited area about each such LBS. The locationbase stations may provide limited voice traffic capabilities, but eachis capable of gathering sufficient wireless signal characteristics froman MS within the location base station's range to facilitate locatingthe MS. Thus, by positioning the location base stations at knownlocations in a geographic region such as, for instance, on street lamppoles and road signs, additional MS location accuracy can be obtained.That is, due to the low power signal output by such location basestations, for there to be signaling control communication (e.g., pilotsignaling and other control signals) between a location base station anda target MS, the MS must be relatively near the location base station.Additionally, for each location base station not in communication withthe target MS, it is likely that the MS is not near to this locationbase station. Thus, by utilizing information received from both locationbase stations in communication with the target MS and those that are notin communication with the target MS, a system and/or method according tothe present disclosure can substantially narrow the possible geographicareas within which the target MS is likely to be. Further, by providingeach location base station (LBS) with a co-located stationary wirelesstransceiver (denoted a built-in MS above) having similar functionalityto an MS, the following advantages are provided:

(6.1) assuming that the co-located base station capabilities and thestationary transceiver of an LBS are such that the base stationcapabilities and the stationary transceiver communicate with oneanother, the stationary transceiver can be signaled by anothercomponent(s) of a novel wireless location system according to thepresent disclosure to activate or deactivate its associated base stationcapability, thereby conserving power for the LBS that operate on arestricted power such as solar electrical power;(6.2) the stationary transceiver of an LBS can be used for transferringtarget MS location information obtained by the LBS to a conventionaltelephony base station;(6.3) since the location of each LBS is known and can be used inlocation processing, a novel wireless location system according to thepresent disclosure is able to (re)train and/or (re)calibrate itself ingeographical areas having such LBSs. That is, by activating each LBSstationary transceiver so that there is signal communication between thestationary transceiver and surrounding base stations within range,wireless signal characteristic values for the location of the stationarytransceiver are obtained for each such base station. Accordingly, suchcharacteristic values can then be associated with the known location ofthe stationary transceiver for training and/or calibrating various ofthe location processing modules of the wireless location system such asthe classification FOMs discussed above. In particular, such trainingand/or calibrating may include:

-   -   (i) (re)training and/or (re)calibrating FOMs;    -   (ii) adjusting the confidence value initially assigned to a        location hypothesis according to how accurate the generating FOM        is in estimating the location of the stationary transceiver        using data obtained from wireless signal characteristics of        signals between the stationary transceiver and base stations        with which the stationary transceiver is capable of        communicating;    -   (iii) automatically updating the previously mentioned historical        data base (i.e., the location signature data base), wherein the        stored signal characteristic data for each stationary        transceiver can be used for detecting environmental and/or        topographical changes (e.g., a newly built high rise or other        structures capable of altering the multipath characteristics of        a given geographical area); and    -   (iv) tuning of the location system parameters, wherein the steps        of: (a) modifying various system parameters and (b) testing the        performance of the modified location system on verified mobile        station location data (including the stationary transceiver        signal characteristic data), these steps being interleaved and        repeatedly performed for obtaining better system location        accuracy within useful time constraints.

It is also an aspect of the present disclosure to automatically(re)calibrate as in (6.3) above with signal characteristics from otherknown or verified locations. In one embodiment of the presentdisclosure, portable location verifying electronics are provided so thatwhen such electronics are sufficiently near a located target MS, theelectronics: (i) detect the proximity of the target MS; (ii) determine ahighly reliable measurement of the location of the target MS; (iii)provide this measurement to other location determining components of anovel wireless location system according to the present disclosure sothat the location measurement can be associated and archived withrelated signal characteristic data received from the target MS at thelocation where the location measurement is performed. Thus, the use ofsuch portable location verifying electronics allows a novel wirelesslocation system according to the present disclosure to capture andutilize signal characteristic data from verified, substantially randomlocations for location system calibration as in (6.3) above. Moreover,it is important to note that such location verifying electronics canverify locations automatically wherein it is unnecessary for manualactivation of a location verifying process.

One embodiment of a wireless location system and/or method according tothe present disclosure includes location verifying electronics such as a“mobile (location) base station” (MBS) that can be, for example,incorporated into a vehicle, such as an ambulance, police car, or taxi.Such a vehicle can travel to sites having a transmitting target MS,wherein such sites may be randomly located and the signal characteristicdata from the transmitting target MS at such a location can consequentlybe archived with a verified location measurement performed at the siteby the mobile location base station. Moreover, it is important to notethat such a mobile location base station as its name implies alsoincludes base station electronics for communicating with mobilestations, though not necessarily in the manner of a conventionalinfrastructure base station. In particular, a mobile location basestation may only monitor signal characteristics, such as MS signalstrength, from a target MS without transmitting signals to the targetMS. Alternatively, a mobile location base station can periodically be inbi-directional communication with a target MS for determining a signaltime-of-arrival (or time-difference-of-arrival) measurement between themobile location base station and the target MS. Additionally, each suchmobile location base station includes components for estimating thelocation of the mobile location base station, such mobile location basestation location estimates being important when the mobile location basestation is used for locating a target MS via, for example,time-of-arrival or time-difference-of-arrival measurements as oneskilled in the art will appreciate. In particular, a mobile locationbase station can include:

(7.1) a mobile station (MS) for both communicating with other componentsof a novel wireless location system according to the present disclosure(such as a location processing center included in the wireless locationsystem);

(7.2) a GPS receiver for determining a location of the mobile locationbase station;

(7.3) a gyroscope and other dead reckoning devices; and

(7.4) devices for operator manual entry of a mobile location basestation location.

Furthermore, a mobile location base station includes modules forintegrating or reconciling distinct mobile location base stationlocation estimates that, for example, can be obtained using thecomponents and devices of (7.1) through (7.4) above. That is, locationestimates for the mobile location base station may be obtained from: GPSsatellite data, mobile location base station data provided by thelocation processing center, deadreckoning data obtained from the mobilelocation base station vehicle deadreckoning devices, and location datamanually input by an operator of the mobile location base station.

The location estimating system of the present disclosure offers manyadvantages over existing location systems. The system of the presentdisclosure, for example, is readily adaptable to existing wirelesscommunication systems and can accurately locate people and/or objects ina cost effective manner. In particular, the present disclosure requiresfew, if any, modifications to commercial wireless communication systemsfor implementation. Thus, existing personal communication systeminfrastructure base stations and other components of, for example,commercial CDMA infrastructures are readily adapted to use a novelwireless location system according to the present disclosure. Thepresent disclosure can be used to locate people and/or objects that arenot in the line-of-sight of a wireless receiver or transmitter, canreduce the detrimental effects of multipath on the accuracy of thelocation estimate, can potentially locate people and/or objects locatedindoors as well as outdoors, and uses a number of wireless stationarytransceivers for location. A novel wireless location system according tothe present disclosure employs a number of distinctly different locationcomputational models for location which provides a greater degree ofaccuracy, robustness and versatility than is possible with existingsystems. For instance, the location models provided include not only theradius-radius/TOA and TDOA techniques but also adaptive artificialneural net techniques. Further, a novel wireless location systemaccording to the present disclosure is able to adapt to the topographyof an area in which location service is desired. A novel wirelesslocation system and/or method according to the present disclosure isalso able to adapt to environmental changes substantially as frequentlyas desired. Thus, a novel wireless location system and/or methodaccording to the present disclosure is able to take into account changesin the location topography over time without extensive manual datamanipulation. Moreover, a novel wireless location system and/or methodaccording to the present disclosure can be utilized with varying amountsof signal measurement inputs. Thus, if a location estimate is desired ina very short time interval (e.g., less than approximately one to twoseconds), then the present location estimating system can be used withonly as much signal measurement data as is possible to acquire during aninitial portion of this time interval. Subsequently, after a greateramount of signal measurement data has been acquired, additional moreaccurate location estimates may be obtained. Note that this capabilitycan be useful in the context of 911 emergency response in that a firstquick coarse wireless mobile station location estimate can be used toroute a 911 call from the mobile station to a 911 emergency responsecenter that has responsibility for the area containing the mobilestation and the 911 caller. Subsequently, once the 911 call has beenrouted according to this first quick location estimate, by continuing toreceive additional wireless signal measurements, more reliable andaccurate location estimates of the mobile station can be obtained.

Moreover, there are numerous additional advantages of the system of thepresent disclosure when applied in CDMA communication systems. Thelocation system of the present disclosure readily benefits from thedistinct advantages of the CDMA spread spectrum scheme, namely, theseadvantages include the exploitation of radio frequency spectralefficiency and isolation by (a) monitoring voice activity, (b)management of two-way power control, (c) provisioning of advancedvariable-rate modems and error correcting signal encoding, (d) inherentresistance to fading, (e) enhanced privacy, and (f) multiple “rake”digital data receivers and searcher receivers for correlation of signalmultipaths.

At a more general level, it is an aspect of the present disclosure todemonstrate the utilization of various novel computational paradigmssuch as:

(8.1) providing a multiple hypothesis computational architecture (asillustrated best in FIGS. 8 and/or FIG. 13) wherein the hypotheses are:

(8.1.1) generated by modular independent hypothesizing computationalmodels;

(8.1.2) the models are embedded in the computational architecture in amanner wherein the architecture allows for substantial amounts ofapplication specific processing common or generic to a plurality of themodels to be straightforwardly incorporated into the computationalarchitecture;

(8.1.3) the computational architecture enhances the hypotheses generatedby the models both according to past performance of the models andaccording to application specific constraints and heuristics withoutrequiring feedback loops for adjusting the models;

(8.1.4) the models are relatively easily integrated into, modified andextracted from the computational architecture;

(8.2) providing a computational paradigm for enhancing an initialestimated solution to a problem by using this initial estimated solutionas, effectively, a query or index into an historical data base ofprevious solution estimates and corresponding actual solutions forderiving an enhanced solution estimate based on past performance of themodule that generated the initial estimated solution.

Note that the multiple hypothesis architecture provided herein is usefulin implementing solutions in a wide range of applications. For example,the following additional applications are within the scope of thepresent disclosure:

(9.1) document scanning applications for transforming physical documentsin to electronic forms of the documents. Note that in many cases thescanning of certain documents (books, publications, etc.) may have a 20%character recognition error rate. Thus, the novel computationarchitecture of the present disclosure can be utilized by (I) providinga plurality of document scanning models as the first order models, (ii)building a character recognition data base for archiving acorrespondence between characteristics of actual printed charactervariations and the intended characters (according to, for example, fonttypes), and additionally archiving a correspondence of performance ofeach of the models on previously encountered actual printed charactervariations (note, this is analogous to the Signature Data Base of the MSlocation application described herein), and (iii) determining anygeneric constraints and/or heuristics that are desirable to be satisfiedby a plurality of the models. Accordingly, by comparing outputs from thefirst order document scanning models, a determination can be made as towhether further processing is desirable due to, for example,discrepancies between the output of the models. If further processing isdesirable, then an embodiment of the multiple hypothesis architectureprovided herein may be utilized to correct such discrepancies. Note thatin comparing outputs from the first order document scanning models,these outputs may be compared at various granularities; e.g., character,sentence, paragraph or page;(9.2) diagnosis and monitoring applications such as medicaldiagnosis/monitoring, communication network diagnosis/monitoring;(9.3) robotics applications such as scene and/or object recognition;(9.4) seismic and/or geologic signal processing applications such as forlocating oil and gas deposits;(9.5) Additionally, note that this architecture need not have allmodules co-located. In particular, it is an additional aspect of thepresent disclosure that various modules can be remotely located from oneanother and communicate with one another via telecommunicationtransmissions such as telephony technologies and/or the Internet.Accordingly, the present disclosure is particularly adaptable to suchdistributed computing environments. For example, some number of thefirst order models may reside in remote locations and communicate theirgenerated hypotheses via the Internet.

For instance, in weather prediction applications it is not uncommon forcomputational models to require large amounts of computationalresources. Thus, such models running at various remote computationalfacilities can transfer weather prediction hypotheses (e.g., the likelypath of a hurricane) to a site that performs hypothesis adjustmentsaccording to: (i) past performance of the each model; (ii) particularconstraints and/or heuristics, and subsequently outputs a most likelyestimate for a particular weather condition.

In an alternative embodiment of the present disclosure, the processingfollowing the generation of location hypotheses (each having an initiallocation estimate) by the first order models may be such that thisprocessing can be provided on Internet user nodes and the first ordermodels may reside at Internet server sites. In this configuration, anInternet user may request hypotheses from such remote first order modelsand perform the remaining processing at his/her node.

In other embodiments of the present disclosure, a fast, albeit lessaccurate location estimate may be initially performed for very timecritical location applications where approximate location informationmay be required. For example, less than 1 second response for a mobilestation location embodiment of the present disclosure may be desired for911 emergency response location requests. Subsequently, once arelatively coarse location estimate has been provided, a more accuratemost likely location estimate can be performed by repeating the locationestimation processing a second time with, e.g., additional withmeasurements of wireless signals transmitted between a mobile station tobe located and a network of base stations with which the mobile stationis communicating, thus providing a second, more accurate locationestimate of the mobile station.

Additionally, note that it is within the scope of the present disclosureto provide one or more central location development sites that may benetworked to, for example, geographically dispersed location centersproviding location services according to the present disclosure, whereinthe FOMs may be accessed, substituted, enhanced or removed dynamicallyvia network connections (via, e.g., the Internet) with a centrallocation development site. Thus, a small but rapidly growingmunicipality in substantially flat low density area might initially beprovided with access to, for example, two or three FOMs for generatinglocation hypotheses in the municipality's relatively uncluttered radiosignaling environment. However, as the population density increases andthe radio signaling environment becomes cluttered by, for example,thermal noise and multipath, additional or alternative FOMs may betransferred via the network to the location center for the municipality.

Note that in some embodiments of the present disclosure, since there isa lack of sequencing between the FOMs and subsequent processing oflocation hypotheses, the FOMs can be incorporated into an expert system,if desired. For example, each FOM may be activated from an antecedent ofan expert system rule. Thus, the antecedent for such a rule can evaluateto TRUE if the FOM outputs a location hypothesis, and the consequentportion of such a rule may put the output location hypothesis on a listof location hypotheses occurring in a particular time window forsubsequent processing by the location center. Alternatively, activationof the FOMs may be in the consequents of such expert system rules. Thatis, the antecedent of such an expert system rule may determine if theconditions are appropriate for invoking the FOM(s) in the rule'sconsequent.

Of course, other software architectures may also be used in implementingthe processing of the location center without departing from scope ofthe present disclosure. In particular, object-oriented architectures arealso within the scope of the present disclosure. For example, the FOMsmay be object methods on an MS location estimator object, wherein theestimator object receives substantially all target MS location signaldata output by the signal filtering subsystem. Alternatively, softwarebus architectures are contemplated by the present disclosure, as oneskilled in the art will understand, wherein the software architecturemay be modular and facilitate parallel processing.

Further features and advantages of the present disclosure are providedby the figures and detailed description accompanying this SummarySection.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates various perspectives of radio propagationopportunities which may be considered in addressing correlation withmobile to base station ranging.

FIG. 2 shows aspects of the two-ray radio propagation model and theeffects of urban clutter.

FIG. 3 provides a typical example of how the statistical power budget iscalculated in design of a Commercial Mobile Radio Service Provider(CMRS) network.

FIG. 4 illustrates an overall view of a wireless radio location networkarchitecture, based on AIN principles.

FIG. 5 is a high level block diagram of an embodiment of a novelwireless location system according to the present disclosure forlocating a mobile station (MS) within a corresponding radio coveragearea.

FIG. 6 is a high level block diagram of the location center 142.

FIG. 7 is a high level block diagram of the hypothesis evaluator for thelocation center.

FIG. 8 is a substantially comprehensive high level block diagramillustrating data and control flows between the components of thelocation center, as well the functionality of the components.

FIGS. 9A and 9B is a high level data structure diagram describing thefields of a location hypothesis object generated by the first ordermodels 1224 of the location center.

FIG. 10 is a graphical illustration of the computation performed by themost likelihood estimator 1344 of the hypothesis evaluator.

FIG. 11 is a high level block diagram of the mobile base station (MBS).

FIG. 12 is a high level state transition diagram describingcomputational states the Mobile Base station enters during operation.

FIG. 13 is a high level diagram illustrating the data structuralorganization of the Mobile Base station capability for autonomouslydetermining a most likely MBS location from a plurality of potentiallyconflicting MBS location estimating sources.

FIG. 14 shows one method of modeling CDMA delay spread measurementensembles and interfacing such signals to a typical artificial neuralnetwork based FOM.

FIG. 15 illustrates the nature of RF “Dead Zones”, notch area, and theimportance of including location data signatures from the back side ofradiating elements.

FIGS. 16 a through 16 c present a table providing a brief description ofthe attributes of the location signature data type stored in thelocation signature data base 1320.

FIGS. 17 a through 17 c present a high level flowchart of the stepsperformed by function, “UPDATE_LOC_SIG_DB,” for updating locationsignatures in the location signature data base 1320; note, thisflowchart corresponds to the description of this function in APPENDIX C.

FIGS. 18 a through 18 b present a high level flowchart of the stepsperformed by function, “REDUCE_BAD_DB_LOC_SIGS,” for updating locationsignatures in the location signature data base 1320; note, thisflowchart corresponds to the description of this function in APPENDIX C.

FIGS. 19 a through 19 b present a high level flowchart of the stepsperformed by function, “INCREASE_CONFIDENCE_OF_GOOD_DB_LOC_SIGS,” forupdating location signatures in the location signature data base 1320;note, this flowchart corresponds to the description of this function inAPPENDIX C.

FIGS. 20 a through 20 d present a high level flowchart of the stepsperformed by function, “DETERMINE_LOCATION_SIGNATURE_FIT_ERRORS,” forupdating location signatures in the location signature data base 1320;note, this flowchart corresponds to the description of this function inAPPENDIX C.

FIG. 21 presents a high level flowchart of the steps performed byfunction, “ESTIMATE_LOC_SIG_FROM_DB,” for updating location signaturesin the location signature data base 1320; note, this flowchartcorresponds to the description of this function in APPENDIX C.

FIGS. 22 a through 22 b present a high level flowchart of the stepsperformed by function, “GET_AREA_TO_SEARCH,” for updating locationsignatures in the location signature data base 1320; note, thisflowchart corresponds to the description of this function in APPENDIX C.

FIGS. 23 a through 23 c present a high level flowchart of the stepsperformed by function, “GET_DIFFERENCE_MEASUREMENT,” for updatinglocation signatures in the location signature data base 1320; note, thisflowchart corresponds to the description of this function in APPENDIX C.

FIG. 24 is a high level illustration of context adjuster data structuresand their relationship to the radio coverage area for a novel wirelesslocation system according to the present disclosure;

FIGS. 25 a through 25 b present a high level flowchart of the stepsperformed by the function, “CONTEXT_ADJUSTER,” used in the contextadjuster 1326 for adjusting mobile station estimates provided by thefirst order models 1224; this flowchart corresponds to the descriptionof this function in APPENDIX D.

FIGS. 26 a through 26 c present a high level flowchart of the stepsperformed by the function, “GET_ADJUSTED_LOC_HYP_LIST_FOR,” used in thecontext adjuster 1326 for adjusting mobile station estimates provided bythe first order models 1224; this flowchart corresponds to thedescription of this function in APPENDIX D.

FIGS. 27 a through 27 b present a high level flowchart of the stepsperformed by the function, “CONFIDENCE_ADJUSTER,” used in the contextadjuster 1326 for adjusting mobile station estimates provided by thefirst order models 1224; this flowchart corresponds to the descriptionof this function in APPENDIX D.

FIGS. 28 a and 28 b presents a high level flowchart of the stepsperformed by the function,“GET_COMPOSITE_PREDICTION_MAPPED_CLUSTER_DENSITY,” used in the contextadjuster 1326 for adjusting mobile station estimates provided by thefirst order models 1224; this flowchart corresponds to the descriptionof this function in APPENDIX D.

FIGS. 29 a through 29 h present a high level flowchart of the stepsperformed by the function, “GET_PREDICTION_MAPPED_CLUSTER_DENSITY_FOR,”used in the context adjuster 1326 for adjusting mobile station estimatesprovided by the first order models 1224; this flowchart corresponds tothe description of this function in APPENDIX D.

FIG. 30 illustrates the primary components of the signal processingsubsystem.

FIG. 31 illustrates how automatic provisioning of mobile stationinformation from multiple CMRS occurs.

DETAILED DESCRIPTION

Detailed Description Introduction

Various digital wireless communication standards have been introducedsuch as Advanced Mobile Phone Service (AMPS), Narrowband Advanced MobilePhone Service (NAMPS), code division multiple access (CDMA) and TimeDivision Multiple Access (TDMA) (e.g., Global Systems Mobile (GSM).These standards provide numerous enhancements for advancing the qualityand communication capacity for wireless applications. Referring to CDMA,this standard is described in the Telephone Industries Associationstandard IS-95, for frequencies below 1 GHz, and in J-STD-008, theWideband Spread-Spectrum Digital Cellular System Dual-Mode MobileStation-Base station Compatibility Standard, for frequencies in the1.8-1.9 GHz frequency bands. Additionally, CDMA general principles havebeen described, for example, in U.S. Pat. No. 5,109,390, DiversityReceiver in a CDMA Cellular Telephone System by Gilhousen. There arenumerous advantages of such digital wireless technologies such as CDMAradio technology. For example, the CDMA spread spectrum scheme exploitsradio frequency spectral efficiency and isolation by monitoring voiceactivity, managing two-way power control, provision of advancedvariable-rate modems and error correcting signal design, and includesinherent resistance to fading, enhanced privacy, and provides formultiple “rake” digital data receivers and searcher receivers forcorrelation of multiple physical propagation paths, resembling maximumlikelihood detection, as well as support for multiple base stationcommunication with a mobile station, i.e., soft or softer hand-offcapability. When coupled with a location center as described herein,substantial improvements in radio location can be achieved. For example,the CDMA spread spectrum scheme exploits radio frequency spectralefficiency and isolation by monitoring voice activity, managing two-waypower control, provision of advanced variable-rate modems and errorcorrecting signal design, and includes inherent resistance to fading,enhanced privacy, and provides for multiple “rake” digital datareceivers and searcher receivers for correlation of multiple physicalpropagation paths, resembling maximum likelihood detection, as well assupport for multiple base station communication with a mobile station,i.e., soft hand-off capability. Moreover, this same advanced radiocommunication infrastructure can also be used for enhanced radiolocation. As a further example, the capabilities of IS-41 and AINalready provide a broad-granularity of wireless location, as isnecessary to, for example, properly direct a terminating call to an MS.Such information, originally intended for call processing usage, can bere-used in conjunction with the location center described herein toprovide wireless location in the large (i.e., to determine whichcountry, state and city a particular MS is located) and wirelesslocation in the small (i.e., which location, plus or minus a few hundredfeet within one or more base stations a given MS is located).

FIG. 4 is a high level diagram of a wireless digital radiolocationintelligent network architecture for a novel wireless location systemaccording to the present disclosure. Accordingly, this figureillustrates the interconnections between the components, for example, ofa typical PCS network configuration and various components that arespecific to an embodiment of al wireless location system according tothe present disclosure. In particular, as one skilled in the art willunderstand, a typical wireless (PCS) network includes:

-   -   (a) a (large) plurality of conventional wireless mobile stations        (MSs) 140 for at least one of voice related communication,        visual (e.g., text) related communication, and according to        present invention, location related communication;    -   (b) a mobile switching center (MSC) 112;    -   (c) a plurality of wireless cell sites in a radio coverage area        120, wherein each cell site includes an infrastructure base        station such as those labeled 122 (or variations thereof such as        122A-122D). In particular, the base stations 122 denote a        standard high traffic, fixed location base stations used for        voice and data communication with a plurality of MSs 140, and        which are also used for communication of information related to        locating such MSs 140. Additionally, note that the base stations        labeled 152 are more directly related to wireless location        enablement. For example, as described in greater detail        hereinbelow, the base stations 152 may be low cost, low        functionality transponders that are used primarily in        communicating MS location related information to the location        center 142 (via base stations 122 and the MSC 112). Note that        unless stated otherwise, the base stations 152 will be referred        to hereinafter as “location base station(s) 152” or simply        “LBS(s) 152”);    -   (d) a public switched telephone network (PSTN) 124 (which may        include signaling system links 106 having network control        components such as: a service control point (SCP) 104, one or        more signaling transfer points (STPs) 110.

Added to this wireless network, the following additional components areprovided:

(10.1) a location center 142 which is required for determining alocation of a target MS 140 using signal characteristic values for thistarget MS;

(10.2) one or more mobile base stations 148 (MBS) which are optional,for physically traveling toward the target MS 140 or tracking the targetMS;

(10.3) a plurality of location base stations 152 (LBS) which areoptional, distributed within the radio coverage areas 120, each LBS 152having a relatively small MS 140 detection area 154;

Since location base stations can be located on potentially each floor ofa multi-story building, the wireless location technology describedherein can be used to perform location in terms of height as well as bylatitude and longitude.

In operation, the MS 140 may utilize one of the wireless technologies,CDMA, TDMA, AMPS, NAMPS or GSM techniques for radio communication with:(a) one or more infrastructure base stations 122, (b) mobile basestation(s) 148, (c) an LBS 152.

Referring to FIG. 4 again, additional detail is provided of typical basestation coverage areas, sectorization, and high level components withina radio coverage area 120, including the MSC 112. Although base stationsmay be placed in any configuration, a typical deployment configurationis approximately in a cellular honeycomb pattern, although manypractical tradeoffs exist, such as site availability, versus therequirement for maximal terrain coverage area. To illustrate, three suchexemplary base stations (BSs) are 122A, 122B and 122C, each of whichradiate referencing signals within their area of coverage 169 tofacilitate mobile station (MS) 140 radio frequency connectivity, andvarious timing and synchronization functions. Note that some basestations may contain no sectors 130 (e.g. 122E), thus radiating andreceiving signals in a 360 degree omnidirectional coverage area pattern,or the base station may contain “smart antennas” which have specializedcoverage area patterns. However, the generally most frequent basestations 122 have three sector 130 coverage area patterns. For example,base station 122A includes sectors 130, additionally labeled a, b and c.Accordingly, each of the sectors 130 radiate and receive signals in anapproximate 120 degree arc, from an overhead view. As one skilled in theart will understand, actual base station coverage areas 169(stylistically represented by hexagons about the base stations 122)generally are designed to overlap to some extent, thus ensuring seamlesscoverage in a geographical area. Control electronics within each basestation 122 are used to communicate with a mobile stations 140.Information regarding the coverage area for each sector 130, such as itsrange, area, and “holes” or areas of no coverage (within the radiocoverage area 120), may be known and used by the location center 142 tofacilitate location determination. Further, during communication with amobile station 140, the identification of each base station 122communicating with the MS 140 as well, as any sector identificationinformation, may be known and provided to the location center 142.

In the case of the base station types 122, 148, and 152 communication oflocation information, a base station or mobility controller 174 (BSC)controls, processes and provides an interface between originating andterminating telephone calls from/to mobile station (MS) 140, and themobile switch center (MSC) 112. The MSC 122, on-the-other-hand, performsvarious administration functions such as mobile station 140registration, authentication and the relaying of various systemparameters, as one skilled in the art will understand.

The base stations 122 may be coupled by various transport facilities 176such as leased lines, frame relay, T-Carrier links, optical fiber linksor by microwave communication links.

When a mobile station 140 (such as a CDMA, AMPS, NAMPS mobile telephone)is powered on and in the idle state, it constantly monitors the pilotsignal transmissions from each of the base stations 122 located atnearby cell sites. Since base station/sector coverage areas may oftenoverlap, such overlapping enables mobile stations 140 to detect, and, inthe case of certain wireless technologies, communicate simultaneouslyalong both the forward and reverse paths, with multiple base stations122 and/or sectors 130. In FIG. 4 the constantly radiating pilot signalsfrom base station sectors 130, such as sectors a, b and c of BS 122A,are detectable by mobile stations 140 within the coverage area 169 forBS 122A. That is, the mobile stations 140 scan for pilot channels,corresponding to a given base station/sector identifiers (IDs), fordetermining which coverage area 169 (i.e., cell) it is contained. Thisis performed by comparing signals strengths of pilot signals transmittedfrom these particular cell-sites.

The mobile station 140 then initiates a registration request with theMSC 112, via the base station controller 174. The MSC 112 determineswhether or not the mobile station 140 is allowed to proceed with theregistration process (except in the case of a 911 call, wherein noregistration process is required). At this point calls may be originatedfrom the mobile station 140 or calls or short message service messagescan be received from the network. The MSC 112 communicates asappropriate, with a class 4/5 wireline telephony circuit switch or othercentral offices, connected to the PSTN 124 network. Such central officesconnect to wireline terminals, such as telephones, or any communicationdevice compatible with the line. The PSTN 124 may also provideconnections to long distance networks and other networks.

The MSC 112 may also utilize IS/41 data circuits or trunks connecting tosignal transfer point 110, which in turn connects to a service controlpoint 104, via Signaling System #7 (SS7) signaling links (e.g., trunks)for intelligent call processing, as one skilled in the art willunderstand. In the case of wireless AIN services such links are used forcall routing instructions of calls interacting with the MSC 112 or anyswitch capable of providing service switching point functions, and thepublic switched telephone network (PSTN) 124, with possible terminationback to the wireless network.

Referring to FIG. 4 again, the location center (LC) 142 interfaces withthe MSC 112 either via dedicated transport facilities 178, using forexample, any number of LAN/WAN technologies, such as Ethernet, fastEthernet, frame relay, virtual private networks, etc., or via the PSTN124. The LC 142 receives autonomous (e.g., unsolicited) command/responsemessages regarding, for example: (a) the state of the wireless networkof each service provider, (b) MS 140 and BS 122 radio frequency (RF)measurements, (c) any MBSs 148, (d) location applications requesting MSlocations using the location center. Conversely, the LC 142 providesdata and control information to each of the above components in (a)-(d).Additionally, the LC 142 may provide location information to an MS 140,via a BS 122. Moreover, in the case of the use of a mobile base station(MBS) 148, several communications paths may exist with the LC 142.

The MBS 148 acts as a low cost, partially-functional, moving basestation, and is, in one embodiment, situated in a vehicle where anoperator may engage in MS 140 searching and tracking activities. Inproviding these activities using CDMA, the MBS 148 provides a forwardlink pilot channel for a target MS 140, and subsequently receives uniqueBS pilot strength measurements from the MS 140. The MBS 148 alsoincludes a mobile station for data communication with the LC 142, via aBS 122. In particular, such data communication includes telemetering thegeographic position of the MBS 148 as well as various RF measurementsrelated to signals received from the target MS 140. In some embodiments,the MBS 148 may also utilize multiple-beam fixed antenna array elementsand/or a moveable narrow beam antenna, such as a microwave dish 182. Theantennas for such embodiments may have a known orientation in order tofurther deduce a radio location of the target MS 140 with respect to anestimated current location of the MBS 148. As will be described in moredetail herein below, the MBS 148 may further contain a globalpositioning system (GPS), distance sensors, deadreckoning electronics,as well as an on-board computing system and display devices for locatingboth the MBS 148 itself as well as tracking and locating the target MS140. The computing and display provides a means for communicating theposition of the target MS 140 on a map display to an operator of the MBS148.

Each location base station (LBS) 152 is a low cost location device. Eachsuch LBS 152 communicates with one or more of the infrastructure basestations 122 using one or more wireless technology interface standards.In some embodiments, to provide such LBS's cost effectively, each LBS152 only partially or minimally supports the air-interface standards ofthe one or more wireless technologies used in communicating with boththe BSs 122 and the MSs 140. Each LBS 152, when put in service, isplaced at a fixed location, such as at a traffic signal, lamp post,etc., and wherein the location of the LBS may be determined asaccurately as, for example, the accuracy of the locations of theinfrastructure BSs 122. Assuming the wireless technology CDMA is used,each BS 122 uses a time offset of the pilot PN sequence to identify aforward CDMA pilot channel. In one embodiment, each LBS 152 emits aunique, time-offset pilot PN sequence channel in accordance with theCDMA standard in the RF spectrum designated for BSs 122, such that thechannel does not interfere with neighboring BSs 122 cell site channels,nor would it interfere with neighboring LBSs 152. However, as oneskilled in the art will understand, time offsets, in CDMA chip sizes,may be re-used within a PCS system, thus providing efficient use ofpilot time offset chips, thereby achieving spectrum efficiency. Each LBS152 may also contain multiple wireless receivers in order to monitortransmissions from a target MS 140. Additionally, each LBS 152 containsmobile station 140 electronics, thereby allowing the LBS to both becontrolled by the LC 142, and to transmit information to the LC 142, viaat least one neighboring BS 122.

As mentioned above, when the location of a particular target MS 140 isdesired, the LC 142 can request location information about the target MS140 from, for instance, one or more activated LBSs 152 in a geographicalarea of interest. Accordingly, whenever the target MS 140 is in such anarea, or is suspected of being in the area, either upon command from theLC 142, or in a substantially continuous fashion, the LBS's pilotchannel appears to the target MS 140 as a potential neighboring basestation channel, and consequently, is placed, for example, in the CDMAneighboring set, or the CDMA remaining set, of the target MS 140 (as onefamiliar with the CDMA standards will understand).

During the normal CDMA pilot search sequence of the mobile stationinitialization state (in the target MS), the target MS 140 will, ifwithin range of such an activated LBS 152, detect the LBS pilot presenceduring the CDMA pilot channel acquisition substrate. Consequently, thetarget MS 140 performs RF measurements on the signal from each detectedLBS 152. Similarly, an activated LBS 152 can perform RF measurements onthe wireless signals from the target MS 140. Accordingly, each LBS 152detecting the target MS 140 may subsequently telemeter back to the LC142 measurement results related to signals from/to the target MS 140.Moreover, upon command, the target MS 140 will telemeter back to the LC142 its own measurements of the detected LBSs 152, and consequently,this new location information, in conjunction with location relatedinformation received from the BSs 122, can be used to locate the targetMS 140.

It should be noted that an LBS 152 will normally deny hand-off requests,since typically the LBS does not require the added complexity ofhandling voice or traffic bearer channels, although economics and peaktraffic load conditions would dictate preference here. GPS timinginformation, needed by any CDMA base station, is either achieved via theinclusion of a local GPS receiver or via a telemetry process from aneighboring conventional BS 122, which contains a GPS receiver andtiming information. Since energy requirements are minimal in such an LBS152, (rechargeable) batteries or solar cells may be used to power theLBS. No expensive terrestrial transport link is typically required sincetwo-way communication is provided by the included MS 140 (or anelectronic variation thereof).

Thus, LBSs 152 may be placed in numerous locations, such as:

-   -   (a) in dense urban canyon areas (e.g., where signal reception        may be poor and/or very noisy);    -   (b) in remote areas (e.g., hiking, camping and skiing areas);    -   (c) along highways (e.g., for emergency as well as monitoring        traffic flow), and their rest stations; or    -   (d) in general, wherever more location precision is required        than is obtainable using other wireless infrastructure network        components.        Location Center—Network Elements API Description

A location application programming interface or L-API 14 (see FIG. 30,and including L-API-Loc_APP 135, L-API-MSC 136, and L-API-SCP 137 shownin FIG. 4), is required between the location center 142 (LC) and themobile switch center (MSC) network element type, in order to send andreceive various control, signals and data messages. The L-API 14 shouldbe implemented using a preferably high-capacity physical layercommunications interface, such as IEEE standard 802.3 (10 baseTEthernet), although other physical layer interfaces could be used, suchas fiber optic ATM, frame relay, etc. Two forms of API implementationare possible. In the first case the signals control and data messagesare realized using the MSC 112 vendor's native operations messagesinherent in the product offering, without any special modifications. Inthe second case the L-API includes a full suite of commands andmessaging content specifically optimized for wireless location purposes,which may require some, although minor development on the part of theMSC vendor.

Signal Processor Description

Referring to FIG. 30, the signal processing subsystem 1220 receivescontrol messages and signal measurements and transmits appropriatecontrol messages to the wireless network via the location applicationsprogramming interface referenced earlier, for wireless locationpurposes. The signal processing subsystem additionally provides varioussignal identification, conditioning and pre-processing functions,including buffering, signal type classification, signal filtering,message control and routing functions to the location estimate modules.

There can be several combinations of Delay Spread/Signal Strength setsof measurements made available to the signal processing subsystem 1220.In some cases the mobile station 140 (FIG. 4) may be able to detect upto three or four Pilot Channels representing three to four BaseStations, or as few as one Pilot Channel, depending upon theenvironment. Similarly, possibly more than one BS 122 can detect amobile station 140 transmitter signal, as evidenced by the provision ofcell diversity or soft hand-off in the CDMA standards, and the fact thatmultiple CMRS' base station equipment commonly will overlap coverageareas. For each mobile station 140 or BS 122 transmitted signal detectedby a receiver group at a station, multiple delayed signals, or “fingers”may be detected and tracked resulting from multipath radio propagationconditions, from a given transmitter.

In typical spread spectrum diversity CDMA receiver design, the “first”finger represents the most direct, or least delayed multipath signal.Second or possibly third or fourth fingers may also be detected andtracked, assuming the mobile station contains a sufficient number ofdata receivers. Although traditional TOA and TDOA methods would discardsubsequent fingers related to the same transmitted finger, collectionand use of these additional values can prove useful to reduce locationambiguity, and are thus collected by the Signal Processing subsystem inthe Location Center 142.

From the mobile receiver's perspective, a number of combinations ofmeasurements could be made available to the Location Center. Due to thedisperse and near-random nature of CDMA radio signals and propagationcharacteristics, traditional TOA/TDOA location methods have failed inthe past, because the number of signals received in different locationsare different. In a particularly small urban area, of say less than 500square feet, the number of RF signals and their multipath components mayvary by over 100 percent.

Due to the large capital outlay costs associated with providing three ormore overlapping base station coverage signals in every possiblelocation, most practical digital PCS deployments result in fewer thanthree base station pilot channels being reportable in the majority oflocation areas, thus resulting in a larger, more amorphous locationestimate. This consequence requires a family of location estimatelocation modules, each firing whenever suitable data has been presentedto a model, thus providing a location estimate to a backend subsystemwhich resolves ambiguities.

In one embodiment of this invention using backend hypothesis resolution,by utilizing existing knowledge concerning base station coverage areaboundaries (such as via the compilation a RF coverage database—eithervia RF coverage area simulations or field tests), the location errorspace is decreased. Negative logic Venn diagrams can be generated whichdeductively rule out certain location estimate hypotheses.

Although the forward link mobile station's received relative signalstrength (RRSS_(BS)) of detected nearby base station transmitter signalscan be used directly by the location estimate modules, the CDMA basestation's reverse link received relative signal strength (RRSS_(MS)) ofthe detected mobile station transmitter signal must be modified prior tolocation estimate model use, since the mobile station transmitter powerlevel changes nearly continuously, and would thus render relative signalstrength useless for location purposes.

One adjustment variable and one factor value are required by the signalprocessing subsystem in the CDMA air interface case: (1) instantaneousrelative power level in dBm (IRPL) of the mobile station transmitter,and (2) the mobile station Power Class. By adding the IRPL to theRRSS_(MS), a synthetic relative signal strength (SRSS_(MS)) of themobile station 140 signal detected at the BS 122 is derived, which canbe used by location estimate model analysis, as shown below:SRSS_(MS)=RRSS_(MS)+IRPL(in dBm)SRSS_(MS), a corrected indication of the effective path loss in thereverse direction (mobile station to BS), is now comparable withRRSS_(BS) and can be used to provide a correlation with either distanceor shadow fading because it now accounts for the change of the mobilestation transmitter's power level. The two signals RRSS_(BS) andSRSS_(MS) can now be processed in a variety of ways to achieve a morerobust correlation with distance or shadow fading.

Although Rayleigh fading appears as a generally random noise generator,essentially destroying the correlation value of either RRSS_(BS) orSRSS_(MS) measurements with distance individually, several mathematicaloperations or signal processing functions can be performed on eachmeasurement to derive a more robust relative signal strength value,overcoming the adverse Rayleigh fading effects. Examples includeaveraging, taking the strongest value and weighting the strongest valuewith a greater coefficient than the weaker value, then averaging theresults. This signal processing technique takes advantage of the factthat although a Rayleigh fade may often exist in either the forward orreverse path, it is much less probable that a Rayleigh fade also existsin the reverse or forward path, respectively. A shadow fade however,similarly affects the signal strength in both paths.

At this point a CDMA radio signal direction independent of “net relativesignal strength measurement” can be derived which can be used toestablish a correlation with either distance or shadow fading, or both.Although the ambiguity of either shadow fading or distance cannot bedetermined, other means can be used in conjunction, such as the fingersof the CDMA delay spread measurement, and any other TOA/TDOAcalculations from other geographical points. In the case of a mobilestation with a certain amount of shadow fading between its BS 122 (FIG.2), the first finger of a CDMA delay spread signal is most likely to bea relatively shorter duration than the case where the mobile station 140and BS 122 are separated by a greater distance, since shadow fading doesnot materially affect the arrival time delay of the radio signal.

By performing a small modification in the control electronics of theCDMA base station and mobile station receiver circuitry, it is possibleto provide the signal processing subsystem 1220 (reference FIG. 30)within the location center 142 (FIG. 1) with data that exceed theone-to-one CDMA delay-spread fingers to data receiver correspondence.Such additional information, in the form of additional CDMA fingers(additional multipath) and all associated detectable pilot channels,provides new information which is used to enhance the accuracy of thelocation center's location estimate location estimate modules.

This enhanced capability is provided via a control message, sent fromthe location center 142 to the mobile switch center 12, and then to thebase station(s) in communication with, or in close proximity with,mobile stations 140 to be located. Two types of location measurementrequest control messages are needed: one to instruct a target mobilestation 140 (i.e., the mobile station to be located) to telemeter its BSpilot channel measurements back to the primary BS 122 and from there tothe mobile switch center 112 and then to the location system 42. Thesecond control message is sent from the location system 42 to the mobileswitch center 112, then to first the primary BS, instructing the primaryBS's searcher receiver to output (i.e., return to the initiating requestmessage source) the detected target mobile station 140 transmitter CDMApilot channel offset signal and their corresponding delay spread finger(peak) values and related relative signal strengths.

The control messages are implemented in standard mobile station 140 andBS 122 CDMA receivers such that all data results from the searchreceiver and multiplexed results from the associated data receivers areavailable for transmission back to the location center 142. Appropriatevalue ranges are required regarding mobile station 140 parametersT_ADD_(s), T_DROP_(s), and the ranges and values for the Active,Neighboring and Remaining Pilot sets registers, held within the mobilestation 140 memory. Further mobile station 140 receiver details havebeen discussed above.

In the normal case without any specific multiplexing means to providelocation measurements, exactly how many CDMA pilot channels and delayspread fingers can or should be measured vary according to the number ofdata receivers contained in each mobile station 140. As a guide, it ispreferred that whenever RF characteristics permit, at least three pilotchannels and the strongest first three fingers, are collected andprocessed. From the BS 122 perspective, it is preferred that thestrongest first four CDMA delay spread fingers and the mobile stationpower level be collected and sent to the location system 42, for each ofpreferably three BSs 122 which can detect the mobile station 140. A muchlarger combination of measurements is potentially feasible using theextended data collection capability of the CDMA receivers.

FIG. 30 illustrates the components of the Signal Processing Subsystem1220 (also shown in FIGS. 5, 6 and 8). The main components consist ofthe input queue(s) 7, signal classifier/filter 9, digital signalingprocessor 17, imaging filters 19, output queue(s) 21, router/distributor23 (also denoted as the “Data Capture And Gateway” in FIG. 8(2)), asignal processor database 26 and a signal processing controller 15.

Input queues 7 are required in order to stage the rapid acceptance of asignificant amount of RF signal measurement data, used for eitherlocation estimate purposes or to accept autonomous location data. Eachlocation request using fixed base stations may, in one embodiment,contain from 1 to 128 radio frequency measurements from the mobilestation, which translates to approximately 61.44 kilobytes of signalmeasurement data to be collected within 10 seconds and 128 measurementsfrom each of possibly four base stations, or 245.76 kilobytes for allbase stations, for a total of approximately 640 signal measurements fromthe five sources, or 307.2 kilobytes to arrive per mobile stationlocation request in 10 seconds. An input queue storage space is assignedat the moment a location request begins, in order to establish aformatted data structure in persistent store. Depending upon the urgencyof the time required to render a location estimate, fewer or more signalmeasurement samples can be taken and stored in the input queue(s) 7accordingly.

The signal processing subsystem 1220 supports a variety of wirelessnetwork signaling measurement capabilities by detecting the capabilitiesof the mobile and base station through messaging structures provided bythe location application programming interface (L-API 14, FIG. 30).Detection is accomplished in the signal classifier 9 (FIG. 30) byreferencing a mobile station database table within the signal processordatabase 26, which provides, given a mobile station identificationnumber, mobile station revision code, other mobile stationcharacteristics. Similarly, a mobile switch center table 31 provides MSCcharacteristics and identifications to the signal classifier/filter 9.The signal classifier/filter adds additional message header informationthat further classifies the measurement data which allows the digitalsignal processor and image filter components to select the properinternal processing subcomponents to perform operations on the signalmeasurement data, for use by the location estimate modules.

Regarding service control point messages (of L-API-SCP interface 137,FIG. 4) autonomously received from the input queue 7 (FIGS. 30 and 31),the signal classifier/filter 9 determines, via a signal processingdatabase 26 query, whether such a message is to be associated with ahome base station module. Thus appropriate header information is addedto the message, thus enabling the message to pass through the digitalsignal processor 17 unaffected to the output queue 21, and then to therouter/distributor 23. The router/distributor 23 then routes the messageto the HBS first order model. Those skilled in the art will understandthat associating location requests from Home Base Station configurationsrequire substantially less data: the mobile identification number andthe associated wireline telephone number transmission from the homelocation register are on the order of less than 32 bytes.Consequentially the home base station message type could be routedwithout any digital signal processing.

Output queue(s) 21 (FIG. 30) are required for similar reasons as inputqueues 7: relatively large amounts of data must be held in a specificformat for further location processing by the location estimate modules1224.

The router and distributor component 23 (FIG. 30) is responsible fordirecting specific signal measurement data types and structures to theirappropriate modules. For example, the HBS FOM has no use for digitalfiltering structures, whereas the TDOA module would not be able toprocess an HBS response message.

The controller 15 (FIG. 30) is responsible for staging the movement ofdata among the signal processing subsystem 1220 components input queue7, digital signal processor 17, router/distributor 23 and the outputqueue 21, and to initiate signal measurements within the wirelessnetwork, in response from an Internet 468 location request message inFIG. 5, via the location application programming interface.

In addition the controller 15 receives autonomous messages from the MSC,via the location applications programming interface or L-API 14 (FIG.30) and the input queue 7, whenever a 911 wireless call is originated.The mobile switch center provides this autonomous notification to thelocation system as follows: by specifying the appropriate mobile switchcenter operations and maintenance commands to surveil calls based oncertain digits dialed such as 911, the location applications programminginterface 14, in communications with the MSCs, receives an autonomousnotification whenever a mobile station user dials 911. Specifically, abi-directional authorized communications port is configured, usually atthe operations and maintenance subsystem of the MSCs, or with theirassociated network element manager system(s), with a data circuit, suchas a DS-1, with the location applications programming interface 14.Next, the “call trace” capability of the mobile switch center isactivated for the respective communications port. The exactimplementation of the vendor-specific man-machine or Open SystemsInterface (OSI) command(s) and their associated data structuresgenerally vary among MSC vendors, however the trace function isgenerally available in various forms, and is required in order to complywith Federal Bureau of Investigation authorities for wire tap purposes.After the appropriate surveillance commands are established on the MSC,such 911 call notifications messages containing the mobile stationidentification number (MIN) and, in U.S. FCC phase 1 E911implementations, a pseudo-automatic number identification (a.k.a. pANI)which provides an association with the primary base station in which the911 caller is in communication. In cases where the pANI is known fromthe onset, the signal processing subsystem 1220 avoids querying the MSCin question to determine the primary base station identificationassociated with the 911 mobile station caller.

After the signal processing controller 15 receives the first messagetype, the autonomous notification message from the mobile switch center112 to the location system 142, containing the mobile identificationnumber and optionally the primary base station identification, thecontroller 15 queries the base station table 13 (FIG. 30) in the signalprocessor database 26 to determine the status and availability of anyneighboring base stations, including those base stations of other CMRSin the area. The definition of neighboring base stations includes notonly those within a provisionable “hop” based on the cell design reusefactor, but also includes, in the case of CDMA, results from remainingset information autonomously queried to mobile stations, with resultsstored in the base station table. Remaining set information indicatesthat mobile stations can detect other base station (sector) pilotchannels which may exceed the “hop” distance, yet are neverthelesscandidate base stations (or sectors) for wireless location purposes.Although cellular and digital cell design may vary, “hop” distance isusually one or two cell coverage areas away from the primary basestation's cell coverage area.

Having determined a likely set of base stations which may both detectthe mobile station's transmitter signal, as well as to determine the setof likely pilot channels (i.e., base stations and their associatedphysical antenna sectors) detectable by the mobile station in the areasurrounding the primary base station (sector), the controller 15initiates messages to both the mobile station and appropriate basestations (sectors) to perform signal measurements and to return theresults of such measurements to the signal processing system regardingthe mobile station to be located. This step may be accomplished viaseveral interface means. In a first case the controller 15 utilizes, fora given MSC, predetermined storage information in the MSC table 31 todetermine which type of commands, such as man-machine or OSI commandsare needed to request such signal measurements for a given MSC. Thecontroller generates the mobile and base station signal measurementcommands appropriate for the MSC and passes the commands via the inputqueue 7 and the locations application programming interface 14 in FIG.30, to the appropriate MSC, using the authorized communications portmentioned earlier. In a second case, the controller 15 communicatesdirectly with base stations within having to interface directly with theMSC for signal measurement extraction.

Upon receipt of the signal measurements, the signal classifier 9 in FIG.30 examines location application programming interface-provided messageheader information from the source of the location measurement (forexample, from a fixed BS 122, a mobile station 140, a distributedantenna system 168 in FIG. 4 or message location data related to a homebase station), provided by the location applications programminginterface (L-API 14) via the input queue 7 in FIG. 30 and determineswhether or not device filters 17 or image filters 19 are needed, andassesses a relative priority in processing, such as an emergency versusa background location task, in terms of grouping like data associatedwith a given location request. In the case where multiple signalmeasurement requests are outstanding for various base stations, some ofwhich may be associated with a different CMRS network, and additionalsignal classifier function includes sorting and associating theappropriate incoming signal measurements together such that the digitalsignal processor 17 processes related measurements in order to buildensemble data sets. Such ensembles allow for a variety of functions suchas averaging, outlier removal over a time period, and related filteringfunctions, and further prevent association errors from occurring inlocation estimate processing.

Another function of the signal classifier/low pass filter component 9 isto filter information that is not useable, or information that couldintroduce noise or the effect of noise in the location estimate modules.Consequently low pass matching filters are used to match the in-commonsignal processing components to the characteristics of the incomingsignals. Low pass filters match: Mobile Station, base station, CMRS andMSC characteristics, as well as to classify Home Base Station messages.

The signal processing subsystem 1220 contains a base station databasetable 13 (FIG. 30) which captures the maximum number of CDMA delayspread fingers for a given base station.

The base station identification code, or CLLI or common language levelidentification code is useful in identifying or relating a human-labeledname descriptor to the Base Station. Latitude, Longitude and elevationvalues are used by other subsystems in the location system forcalibration and estimation purposes. As base stations and/or receivercharacteristics are added, deleted, or changed with respect to thenetwork used for location purposes, this database table must be modifiedto reflect the current network configuration.

Just as an upgraded base station may detect additional CDMA delay spreadsignals, newer or modified mobile stations may detect additional pilotchannels or CDMA delay spread fingers. Additionally different makes andmodels of mobile stations may acquire improved receiver sensitivities,suggesting a greater coverage capability. A table may establish therelationships among various mobile station equipment suppliers andcertain technical data relevant to this location invention.

Although not strictly necessary, the MIN can be populated in this tablefrom the PCS Service Provider's Customer Care system during subscriberactivation and fulfillment, and could be changed at deactivation, oranytime the end-user changes mobile stations. Alternatively, since theMIN, manufacturer, model number, and software revision level informationis available during a telephone call, this information could extractedduring the call, and the remaining fields populated dynamically, basedon manufacturer's' specifications information previously stored in thesignal processing subsystem 1220. Default values are used in cases wherethe MIN is not found, or where certain information must be estimated.

A low pass mobile station filter, contained within the signalclassifier/low pass filter 9 of the signal processing subsystem 1220,uses the above table data to perform the following functions: 1) act asa low pass filter to adjust the nominal assumptions related to themaximum number of CDMA fingers, pilots detectable; and 2) to determinethe transmit power class and the receiver thermal noise floor. Given thedetected reverse path signal strength, the required value of SRSS_(MS),a corrected indication of the effective path loss in the reversedirection (mobile station to BS), can be calculated based on datacontained within the mobile station table 11, stored in the signalprocessing database 26.

The effects of the maximum number of CDMA fingers allowed and themaximum number of pilot channels allowed essentially form a low passfilter effect, wherein the least common denominator of characteristicsare used to filter the incoming RF signal measurements such that a onefor one matching occurs. The effect of the transmit power class andreceiver thermal noise floor values is to normalize the characteristicsof the incoming RF signals with respect to those RF signals used.

The signal classifier/filter 9 (FIG. 30) is in communication with boththe input queue 7 and the signal processing database 26. In the earlystage of a location request the signal processing subsystem 1220 shownin, e.g., FIGS. 5, 30 and 31, will receive the initiating locationrequest from either an autonomous 911 notification message from a givenMSC, or from a location application, for which mobile stationcharacteristics about the target mobile station 140 (FIG. 4) isrequired. Referring to FIG. 30, a query is made from the signalprocessing controller 15 to the signal processing database 26,specifically the mobile station table 11, to determine if the mobilestation characteristics associated with the MIN to be located isavailable in table 11. If the data exists then there is no need for thecontroller 15 to query the wireless network in order to determine themobile station characteristics, thus avoiding additional real-timeprocessing which would otherwise be required across the air interface,in order to determine the mobile station MIN characteristics. Theresulting mobile station information my be provided either via thesignal processing database 26 or alternatively a query may be performeddirectly from the signal processing subsystem 1220 to the MSC in orderto determine the mobile station characteristics.

Referring now to FIG. 31, another location application programminginterface, L-API-CCS 239 to the appropriate CMRS customer care systemprovides the mechanism to populate and update the mobile station table11 within the database 26. The L-API-CCS 239 contains its own set ofseparate input and output queues or similar implementations and securitycontrols to ensure that provisioning data is not sent to the incorrectCMRS, and that a given CMRS cannot access any other CMRS' data. Theinterface 1155 a to the customer care system for CMRS-A 1150 a providesan autonomous or periodic notification and response application layerprotocol type, consisting of add, delete, change and verify messagefunctions in order to update the mobile station table 11 within thesignal processing database 26, via the controller 15. A similarinterface 155 b is used to enable provisioning updates to be receivedfrom CMRS-B customer care system 1150 b.

Although the L-API-CCS application message set may be any protocol typewhich supports the autonomous notification message with positiveacknowledgment type, the T1M1.5 group within the American NationalStandards Institute has defined a good starting point in which theL-API-CCS 239 could be implemented, using the robust OSI TMN X-interfaceat the service management layer. The object model defined in Standardsproposal number T1M1.5/96-22R9, Operations Administration, Maintenance,and Provisioning (OAM&P)—Model for Interface Across JurisdictionalBoundaries to Support Electronic Access Service Ordering: InquiryFunction, can be extended to support the L-API-CCS information elementsas required and further discussed below. Other choices in which theL-API-CCS application message set may be implemented include ASCII,binary, or any encrypted message set encoding using the Internetprotocols, such as TCP/IP, simple network management protocol, http,https, and email protocols.

Referring to the digital signal processor (DSP) 17, in communicationwith the signal classifier/LP filter 9, the DSP 17 provides a timeseries expansion method to convert non-HBS data from a format of ansignal measure data ensemble of time-series based radio frequency datameasurements, collected as discrete time-slice samples, to a threedimensional matrix location data value image representation. Othertechniques further filter the resultant image in order to furnish a lessnoisy training and actual data sample to the location estimate modules.

After 128 samples (in one embodiment) of data are collected of the delayspread-relative signal strength RF data measurement sample, mobilestation RX for BS-1 are grouped into a quantization matrix, where rowsconstitute relative signal strength intervals and columns define delayintervals. Each measurement row, column pair (which could be representedas a complex number or Cartesian point pair) is added to theirrespective values to generate a Z direction of frequency of recurringmeasurement value pairs or a density recurrence function. By nextapplying a grid function to each x, y, and z value, a three-dimensionalsurface grid is generated, which represents a location data value orunique print of that 128-sample measurement.

In the general case where a mobile station is located in an environmentwith varied clutter patterns, such as terrain undulations, uniqueman-made structure geometries (thus creating varied multipath signalbehaviors), such as a city or suburb, although the first CDMA delayspread finger may be the same value for a fixed distance between themobile station and BS antennas, as the mobile station moves across suchan area, different finger-data are measured. In the right image for thedefined BS antenna sector, location classes, or squares numbered onethrough seven, are shown across a particular range of line of position(LOP).

A traditional TOA/TDOA ranging method between a given BS and mobilestation only provides a range along an arc, thus introducing ambiguityerror. However a unique three dimensional image can be used in thismethod to specifically identify, with recurring probability, aparticular unique location class along the same Line Of Position, aslong as the multipath is unique by position but generally repeatable,thus establishing a method of not only ranging, but also of completelatitude, longitude location estimation in a Cartesian space. In otherwords, the unique shape of the “mountain image” enables a correspondenceto a given unique location class along a line of position, therebyeliminating traditional ambiguity error.

Although man-made external sources of interference, Rayleigh fades,adjacent and co-channel interference, and variable clutter, such asmoving traffic introduce unpredictability (thus no “mountain image”would ever be exactly alike), three basic types of filtering methods canbe used to reduce matching/comparison error from a training case to alocation request case: (1) select only the strongest signals from theforward path (BS to mobile station) and reverse path (mobile station toBS), (2) convolute the forward path 128 sample image with the reversepath 128 sample image, and (3) process all image samples through variousdigital image filters to discard noise components.

In one embodiment, convolution of forward and reverse images isperformed to drive out noise. This is one embodiment that essentiallynulls noise completely, even if strong and recurring, as long as thatsame noise characteristic does not occur in the opposite path.

The third embodiment or technique of processing CDMA delay spreadprofile images through various digital image filters, provides aresultant “image enhancement” in the sense of providing a more stablepattern recognition paradigm to the neural net location estimate model.For example, image histogram equalization can be used to rearrange theimages' intensity values, or density recurrence values, so that theimage's cumulative histogram is approximately linear.

Other methods which can be used to compensate for a concentratedhistogram include: (1) Input Cropping, (2) Output Cropping and (3) GammaCorrection. Equalization and input cropping can provide particularlystriking benefits to a CDMA delay spread profile image. Input croppingremoves a large percentage of random signal characteristics that arenon-recurring.

Other filters and/or filter combinations can be used to help distinguishbetween stationary and variable clutter affecting multipath signals. Forexample, it is desirable to reject multipath fingers associated withvariable clutter, since over a period of a few minutes such fingerswould not likely recur. Further filtering can be used to removerecurring (at least during the sample period), and possibly strong butnarrow “pencils” of RF energy. A narrow pencil image component could berepresented by a near perfect reflective surface, such as a nearby metalpanel truck stopped at a traffic light.

On the other hand, stationary clutter objects, such as concrete andglass building surfaces, adsorb some radiation before continuing with areflected ray at some delay. Such stationary clutter-affected CDMAfingers are more likely to pass a 4×4 neighbor Median filter as well asa 40 to 50 percent Input Crop filter, and are thus more suited to neuralnet pattern recognition. However when subjected to a 4×4 neighbor Medianfilter and 40 percent clipping, pencil-shaped fingers are deleted. Othercombinations include, for example, a 50 percent cropping and 4×4neighbor median filtering. Other filtering methods include custom linearfiltering, adaptive (Weiner) filtering, and custom nonlinear filtering.

The DSP 17 may provide data ensemble results, such as extracting theshortest time delay with a detectable relative signal strength, to therouter/distributor 23, or alternatively results may be processed via oneor more image filters 19, with subsequent transmission to therouter/distributor 23. The router/distributor 23 examines the processedmessage data from the DSP 17 and stores routing and distributioninformation in the message header. The router/distributor 23 thenforwards the data messages to the output queue 21, for subsequentqueuing then transmission to the appropriate location estimator FOMs.

Location Center High Level Functionality

At a very high level the location center 142 computes location estimatesfor a wireless Mobile Station 140 (denoted the “target MS” or “MS”) byperforming the following steps:

(23.1) receiving signal transmission characteristics of communicationscommunicated between the target MS 140 and one or more wirelessinfrastructure base stations 122;

(23.2) filtering the received signal transmission characteristics (by asignal processing subsystem 1220 illustrated in FIG. 5) as needed sothat target MS location data can be generated that is uniform andconsistent with location data generated from other target MSs 140. Inparticular, such uniformity and consistency is both in terms of datastructures and interpretation of signal characteristic values providedby the MS location data;(23.3) inputting the generated target MS location data to one or more MSlocation estimating models (denoted First order models or FOMs, andlabeled collectively as 1224 in FIG. 5), so that each such model may usethe input target MS location data for generating a “location hypothesis”providing an estimate of the location of the target MS 140;(23.4) providing the generated location hypotheses to an hypothesisevaluation module (denoted the hypothesis evaluator 1228 in FIG. 5):

(a) for adjusting at least one of the target MS location estimates ofthe generated location hypotheses and related confidence valuesindicating the confidence given to each location estimate, wherein suchadjusting uses archival information related to the accuracy ofpreviously generated location hypotheses,

(b) for evaluating the location hypotheses according to variousheuristics related to, for example, the radio coverage area 120 terrain,the laws of physics, characteristics of likely movement of the target MS140; and

(c) for determining a most likely location area for the target MS 140,wherein the measurement of confidence associated with each input MSlocation area estimate is used for determining a “most likely locationarea”; and

(23.5) outputting a most likely target MS location estimate to one ormore applications 146 (FIG. 5) requesting an estimate of the location ofthe target MS 140.

Location Hypothesis Data Representation

In order to describe how the steps (23.1) through (23.5) are performedin the sections below, some introductory remarks related to the datadenoted above as location hypotheses will be helpful. Additionally, itwill also be helpful to provide introductory remarks related tohistorical location data and the data base management programsassociated therewith.

For each target MS location estimate generated and utilized by anembodiment of a wireless location system according to the presentdisclosure, the location estimate is provided in a data structure (orobject class) denoted as a “location hypothesis” (illustrated in TableLH-1). Although brief descriptions of the data fields for a locationhypothesis is provided in the Table LH-1, many fields require additionalexplanation. Accordingly, location hypothesis data fields are furtherdescribed as noted below.

TABLE LH-1 FOM_ID First order model ID (providing this LocationHypothesis); note, since it is possible for location hypotheses to begenerated by other than the FOMs 1224, in general, this field identifiesthe module that generated this location hypothesis. MS_ID Theidentification of the target MS 140 to this location hypothesis applies.pt_est The most likely location point estimate of the target MS 140.valid_pt Boolean indicating the validity of “pt_est”. area_est LocationArea Estimate of the target MS 140 provided by the FOM. This areaestimate will be used whenever “image_area” below is NULL. valid_areaBoolean indicating the validity of “area_est” (one of “pt_est” and“area_est” must be valid). adjust Boolean (true if adjustments to thefields of this location hypothesis are to be performed in the Contextadjuster Module). pt_covering Reference to a substantially minimal area(e.g., mesh cell) covering of “pt_est”. Note, since this MS 140 may besubstantially on a cell boundary, this covering may, in some cases,include more than one cell. image_area Reference to a substantiallyminimal area (e.g., mesh cell) covering of “pt_covering” (see detaileddescription of the function, “confidence_adjuster”). Note that if thisfield is not NULL, then this is the target MS location estimate used bythe location center 142 instead of “area_est”. extrapolation_areaReference to (if non-NULL) an extrapolated MS target estimate areaprovided by the location extrapolator submodule 1432 of the hypothesisanalyzer 1332. That is, this field, if non-NULL, is an extrapolation ofthe “image_area” field if it exists, otherwise this field is anextrapolation of the “area_est” field. Note other extrapolation fieldsmay also be provided depending on the embodiment of the presentdisclosure, such as an extrapolation of the “pt_covering”. confidence Areal value in the range [−1.0, +1.0] indicating a likelihood that thetarget MS 140 is in (or out) of a particular area. If positive: if“image_area” exists, then this is a measure of the likelihood that thetarget MS 140 is within the area represented by “image_area”, or if“image_area” has not been computed (e.g., “adjust” is FALSE), then“area_est” must be valid and this is a measure of the likelihood thatthe target MS 140 is within the area represented by “area_est”. Ifnegative, then “area_est” must be valid and this is a measure of thelikelihood that the target MS 140 is NOT in the area represented by“area_est”. If it is zero (near zero), then the likelihood is unknown.Original_Timestamp Date and time that the location signature cluster(defined hereinbelow) for this location hypothesis was received by thesignal processing subsystem 1220. Active_Timestamp Run-time fieldproviding the time to which this location hypothesis has had its MSlocation estimate(s) extrapolated (in the location extrapolator 1432 ofthe hypothesis analyzer 1332). Note that this field is initialized withthe value from the “Original_Timestamp” field. Processing Tags and Forindicating particular types of environmental classifications not readilyenvironmental categorizations determined by the “Original Timestamp”field (e.g., weather, traffic), and restrictions on location hypothesisprocessing. loc_sig_cluster Provides access to the collection oflocation signature signal characteristics derived from communicationsbetween the target MS 140 and the base station(s) detected by this MS(discussed in detail hereinbelow); in particular, the location dataaccessed here is provided to the first order models by the signalprocessing subsystem 1220; i.e., access to the “loc sigs” (received at“timestamp” regarding the location of the target MS) descriptor Originaldescriptor (from the First order model indicating why/how the LocationArea Estimate and Confidence Value were determined).

As can be seen in the Table LH-1, each location hypothesis datastructure includes at least one measurement, denoted hereinafter as aconfidence value (or simply confidence), that is a measurement of theperceived likelihood that an MS location estimate in the locationhypothesis is an accurate location estimate of the target MS 140. Sincesuch confidence values are an important aspect of the presentdisclosure, much of the description and use of such confidence valuesare described below; however, a brief description is provided here. Eachsuch confidence value is in the range −1.0 to 1.0, wherein the largerthe value, the greater the perceived likelihood that the target MS 140is in (or at) a corresponding MS location estimate of the locationhypothesis to which the confidence value applies. As an aside, note thata location hypothesis may have more than one MS location estimate (aswill be discussed in detail below) and the confidence value willtypically only correspond or apply to one of the MS location estimatesin the location hypothesis. Further, values for the confidence valuefield may be interpreted as: (a) −1.0 may be interpreted to mean thatthe target MS 140 is NOT in such a corresponding MS area estimate of thelocation hypothesis area, (b) 0 may be interpreted to mean that it isunknown as to the likelihood of whether the MS 140 in the correspondingMS area estimate, and (c) +1.0 may be interpreted to mean that the MS140 is perceived to positively be in the corresponding MS area estimate.

Additionally, note that it is within the scope of the present disclosurethat the location hypothesis data structure may also include otherrelated “perception” measurements related to a likelihood of the targetMS 140 being in a particular MS location area estimate. For example, itis within the scope of the present disclosure to also utilizemeasurements such as, (a) “sufficiency factors” for indicating thelikelihood that an MS location estimate of a location hypothesis issufficient for locating the target MS 140; (b) “necessity factors” forindicating the necessity that the target MS be in an particular areaestimate. However, to more easily describe embodiments of wirelesslocation systems and/or methods according to the present disclosure, asingle confidence field is used having the interpretation given above.

Additionally, in utilizing location hypotheses in, for example, thelocation evaluator 1228 as in (23.4) above, it is important to keep inmind that each location hypothesis confidence value is a relativemeasurement. That is, for confidences, cf₁ and cf₂, if cf₁<=cf₂, thenfor a location hypotheses H₁ and H₂ having cf₁ and cf₂, respectively,the target MS 140 is expected to more likely reside in a target MSestimate of H₂ than a target MS estimate of H₁. Moreover, if an area, A,is such that it is included in a plurality of location hypothesis targetMS estimates, then a confidence score, CS_(A), can be assigned to A,wherein the confidence score for such an area is a function of theconfidences (both positive and negative) for all the location hypotheseswhose (most pertinent) target MS location estimates contain A. That is,in order to determine a most likely target MS location area estimate foroutputting from the location center 142, a confidence score isdetermined for areas within the location center service area. Moreparticularly, if a function, “f”, is a function of the confidence(s) oflocation hypotheses, and f is a monotonic function in its parameters andf(cf₁, cf₂, cf₃, . . . , cf_(N))=CS_(A) for confidences cf_(i) oflocation hypotheses H_(i), i=1, 2, . . . , N, with A contained in thearea estimate for H_(i), then “f” is denoted a confidence scorefunction. Accordingly, there are many embodiments for a confidence scorefunction f that may be utilized in computing confidence scores forembodiments of wireless location systems and/or methods disclosedherein; e.g.,

(a) f(cf₁, cf₂, . . . , cf_(N))=Σcf_(i)=CS_(A);

(b) f(cf₁, cf₂, . . . , cf_(N))=Σcf_(i) ^(n)=CS_(A), n=1, 3, 5, . . . ;

(c) f(cf₁, cf₂, . . . , cf_(N))=Σ(K_(i)*cf_(i))=CS_(A), wherein K_(i),i=1, 2, . . . are positive system (tunable) constants (possiblydependent on environmental characteristics such as topography, time,date, traffic, weather, and/or the type of base station(s) 122 fromwhich location signatures with the target MS 140 are being generated,etc.).

For the present description of the invention, the function f as definedin (c) immediately above is utilized. However, for obtaining a generalunderstanding of the present disclosure, the simpler confidence scorefunction of (a) may be more useful. It is important to note, though,that it is within the scope of the present disclosure to use otherfunctions for the confidence score function.

Coverage Area: Area Types and Their Determination

The notion of “area type” as related to wireless signal transmissioncharacteristics has been used in many investigations of radio signaltransmission characteristics. Some investigators, when investigatingsuch signal characteristics of areas have used somewhat naive areaclassifications such as urban, suburban, rural, etc. However, it isdesirable for the purposes of the present disclosure to have a moreoperational definition of area types that is more closely associatedwith wireless signal transmission behaviors.

To describe embodiments of the an area type scheme used in the presentdisclosure, some introductory remarks are first provided. Note that thewireless signal transmission behavior for an area depends on at leastthe following criteria:

-   -   (23.8.1) substantially invariant terrain characteristics (both        natural and man-made) of the area; e.g., mountains, buildings,        lakes, highways, bridges, building density;    -   (23.8.2) time varying environmental characteristics (both        natural and man-made) of the area; e.g., foliage, traffic,        weather, special events such as baseball games;    -   (23.8.3) wireless communication components or infrastructure in        the area; e.g., the arrangement and signal communication        characteristics of the base stations 122 in the area. Further,        the antenna characteristics at the base stations 122 may be        important criteria.

Accordingly, a description of wireless signal characteristics fordetermining area types could potentially include a characterization ofwireless signaling attributes as they relate to each of the abovecriteria. Thus, an area type might be: hilly, treed, suburban, having nobuildings above 50 feet, with base stations spaced apart by two miles.However, a categorization of area types is desired that is both moreclosely tied to the wireless signaling characteristics of the area, andis capable of being computed substantially automatically and repeatedlyover time. Moreover, for a wireless location system, the primarywireless signaling characteristics for categorizing areas into at leastminimally similar area types are: thermal noise and, more importantly,multipath characteristics (e.g., multipath fade and time delay).

Focusing for the moment on the multipath characteristics, it is believedthat (23.8.1) and (23.8.3) immediately above are, in general, moreimportant criteria for accurately locating an MS 140 than (23.8.2). Thatis, regarding (23.8.1), multipath tends to increase as the density ofnearby vertical area changes increases. For example, multipath isparticularly problematic where there is a high density of high risebuildings and/or where there are closely spaced geographic undulations.In both cases, the amount of change in vertical area per unit of area ina horizontal plane (for some horizontal reference plane) may be high.Regarding (23.8.3), the greater the density of base stations 122, theless problematic multipath may become in locating an MS 140. Moreover,the arrangement of the base stations 122 in the radio coverage area 120in FIG. 4 may affect the amount and severity of multipath.

Accordingly, it would be desirable to have a method and system forstraightforwardly determining area type classifications related tomultipath, and in particular, multipath due to (23.8.1) and (23.8.3).The present disclosure provides such a determination by utilizing anovel notion of area type, hereinafter denoted “transmission area type”(or, “(transmission) area type” when both a generic area typeclassification scheme and the transmission area type discussedhereinafter are intended) for classifying “similar” areas, wherein eachtransmission area type class or category is intended to describe an areahaving at least minimally similar wireless signal transmissioncharacteristics. That is, the novel transmission area type scheme of thepresent disclosure is based on: (a) the terrain area classifications;e.g., the terrain of an area surrounding a target MS 140, (b) theconfiguration of base stations 122 in the radio coverage area 120, and(c) characterizations of the wireless signal transmission paths betweena target MS 140 location and the base stations 122.

In one embodiment of a method and system for determining such(transmission) area type approximations, a partition (denotedhereinafter as P₀) is imposed upon the radio coverage area 120 forpartitioning for radio coverage area into subareas, wherein each subareais an estimate of an area having included MS 140 locations that arelikely to have is at least a minimal amount of similarity in theirwireless signaling characteristics. To obtain the partition P₀ of theradio coverage area 120, the following steps are performed:

-   -   (23.8.4.1) Partition the radio coverage area 120 into subareas,        wherein in each subarea is: (a) connected, (b) variations in the        lengths of chords sectioning the subarea through the centroid of        the subarea are below a predetermined threshold, (c) the subarea        has an area below a predetermined value, and (d) for most        locations (e.g., within a first or second standard deviation)        within the subarea whose wireless signaling characteristics have        been verified, it is likely (e.g., within a first or second        standard deviation) that an MS 140 at one of these locations        will detect (forward transmission path) and/or will be detected        (reverse transmission path) by a same collection of base        stations 122. For example, in a CDMA context, a first such        collection may be (for the forward transmission path) the active        set of base stations 122, or, the union of the active and        candidate sets, or, the union of the active, candidate and/or        remaining sets of base stations 122 detected by “most” MSs 140        in the subarea. Additionally (or alternatively), a second such        collection may be the base stations 122 that are expected to        detect MSs 140 at locations within the subarea. Of course, the        union or intersection of the first and second collections is        also within the scope of the present disclosure for partitioning        the radio coverage area 120 according to (d) above. It is worth        noting that it is believed that base station 122 power levels        will be substantially constant. However, even if this is not the        case, one or more collections for (d) above may be determined        empirically and/or by computationally simulating the power        output of each base station 122 at a predetermined level.        Moreover, it is also worth mentioning that this step is        relatively straightforward to implement using the data stored in        the location signature data base 1320 (i.e., the verified        location signature clusters discussed in detail hereinbelow).        Denote the resulting partition here as P₁.    -   (23.8.4.2) Partition the radio coverage area 120 into subareas,        wherein each subarea appears to have substantially homogeneous        terrain characteristics. Note, this may be performed        periodically substantially automatically by scanning radio        coverage area images obtained from aerial or satellite imaging.        For example, EarthWatch Inc. of Longmont, Colo. can provide        geographic with 3 meter resolution from satellite imaging data.        Denote the resulting partition here as P₂.    -   (23.8.4.3) Overlay both of the above partitions of the radio        coverage area 120 to obtain new subareas that are intersections        of the subareas from each of the above partitions. This new        partition is P₀ (i.e., P₀=P₁ intersect P₂), and the subareas of        it are denoted as “P₀ subareas”.

Now assuming P₀ has been obtained, the subareas of P₀ are provided witha first classification or categorization as follows:

-   -   (23.8.4.4) Determine an area type categorization scheme for the        subareas of P₁. For example, a subarea, A, of P₁, may be        categorized or labeled according to the number of base stations        122 in each of the collections used in (23.8.4.1)(d) above for        determining subareas of P₁. Thus, in one such categorization        scheme, each category may correspond to a single number x (such        as 3), wherein for a subarea, A, of this category, there is a        group of x (e.g., three) base stations 122 that are expected to        be detected by a most target MSs 140 in the area A. Other        embodiments are also possible, such as a categorization scheme        wherein each category may correspond to a triple: of numbers        such as (5, 2, 1), wherein for a subarea A of this category,        there is a common group of 5 base stations 122 with two-way        signal detection expected with most locations (e.g., within a        first or second deviation) within A, there are 2 base stations        that are expected to be detected by a target MS 140 in A but        these base stations can not detect the target MS, and there is        one base station 122 that is expected to be able to detect a        target MS in A but not be detected.    -   (23.8.4.5) Determine an area type categorization scheme for the        subareas of P₂. Note that the subareas of P₂ may be categorized        according to their similarities. In one embodiment, such        categories may be somewhat similar to the naive area types        mentioned above (e.g., dense urban, urban, suburban, rural,        mountain, etc.). However, it is also an aspect of the present        disclosure that more precise categorizations may be used, such        as a category for all areas having between 20,000 and 30,000        square feet of vertical area change per 11,000 square feet of        horizontal area and also having a high traffic volume (such a        category likely corresponding to a “moderately dense urban” area        type).    -   (23.8.4.6) Categorize subareas of P₀ with a categorization        scheme denoted the “P₀ categorization,” wherein for each P₀        subarea, A, of P₀ a “P₀ area type” is determined for A according        to the following substep(s):        -   (a) Categorize A by the two categories from (23.8.4.4) and            (23.8.5) with which it is identified. Thus, A is categorized            (in a corresponding P₀ area type) both according to its            terrain and the base station infrastructure configuration in            the radio coverage area 120.    -   (23.8.4.7) For each P₀ subarea, A, of P₀ perform the following        step(s):        -   (a) Determine a centroid, C(A), for A;        -   (b) Determine an approximation to a wireless transmission            path between C(A) and each base station 122 of a            predetermined group of base stations expected to be in (one            and/or two-way) signal communication with most target MS 140            locations in A. For example, one such approximation is a            straight line between C(A) and each of the base stations 122            in the group. However, other such approximations are within            the scope of the present disclosure, such as, a generally            triangular shaped area as the transmission path, wherein a            first vertex of this area is at the corresponding base            station for the transmission path, and the sides of the            generally triangular shaped defining the first vertex have a            smallest angle between them that allows A to be completely            between these sides.        -   (c) For each base station 122, BS_(i), in the group            mentioned in (b) above, create an empty list, BS_(i)-list,            and put on this list at least the P₀ area types for the            “significant” P₀ subareas crossed by the transmission path            between C(A) and BS_(i). Note that “significant” P₀ subareas            may be defined as, for example, the P₀ subareas through            which at least a minimal length of the transmission path            traverses. Alternatively, such “significant” P₀ subareas may            be defined as those P₀ subareas that additionally are known            or expected to generate substantial multipath.        -   (d) Assign as the transmission area type for A as the            collection of BS_(i)-lists. Thus, any other P₀ subarea            having the same (or substantially similar) collection of            lists of P₀ area types will be viewed as having            approximately the same radio transmission characteristics.

Note that other transmission signal characteristics may be incorporatedinto the transmission area types. For example, thermal noisecharacteristics may be included by providing a third radio coverage area120 partition, P₃, in addition to the partitions of P₁ and P₂ generatedin (23.8.4.1) and (23.8.4.2) respectively. Moreover, the time varyingcharacteristics of (23.8.2) may be incorporated in the transmission areatype frame work by generating multiple versions of the transmission areatypes such that the transmission area type for a given subarea of P₀ maychange depending on the combination of time varying environmentalcharacteristics to be considered in the transmission area types. Forinstance, to account for seasonality, four versions of the partitions P₁and P₂ may be generated, one for each of the seasons, and subsequentlygenerate a (potentially) different partition P₀ for each season.Further, the type and/or characteristics of base station 122 antennasmay also be included in an embodiment of the transmission area type.

Accordingly, in one embodiment of the present disclosure, whenever theterm “area type” is used hereinbelow, transmission area types asdescribed hereinabove are intended.

Location Information Data Bases and Data

Location Data Bases Introduction

It is an aspect of the present disclosure that MS location processingperformed by the location center 142 should become increasingly betterat locating a target MS 140 both by (a) building an increasingly moredetailed model of the signal characteristics of locations in thewireless service area for the location center 142 (and/or its FOMs), andalso (b) by providing capabilities for the location center processing toadapt to environmental changes.

One way these aspects of the present disclosure are realized is byproviding one or more data base management systems and data bases for:

(a) storing and associating wireless MS signal characteristics withknown locations of MSs 140 used in providing the signal characteristics.Such stored associations may not only provide an increasingly bettermodel of the signal characteristics of the geography of the servicearea, but also provide an increasingly better model of more changeablesignal characteristics affecting environmental factors such as weather,seasons, and/or traffic patterns;

(b) adaptively updating the signal characteristic data stored so that itreflects changes in the environment of the service area such as, forexample, a new high rise building or a new highway.

Referring again to FIG. 5 of the collective representation of these databases is the location information data bases 1232. Included among thesedata bases is a data base for providing training and/or calibration datato one or more trainable/calibratable FOMs 1224, as well as an archivaldata base for archiving historical MS location information related tothe performance of the FOMs. These data bases will be discussed asnecessary hereinbelow. However, a further brief introduction to thearchival data base is provided here. Accordingly, the term, “locationsignature data base” is used hereinafter to denote the archival database and/or data base management system depending on the context of thediscussion. The location signature data base (shown in, for example,FIG. 6 and labeled 1320) is a repository for wireless signalcharacteristic data derived from wireless signal communications betweenan MS 140 and one or more base stations 122, wherein the correspondinglocation of the MS 140 is known and also stored in the locationsignature data base 1320. More particularly, the location signature database 1320 associates each such known MS location with the wirelesssignal characteristic data derived from wireless signal communicationsbetween the MS 140 and one or more base stations 122 at this MSlocation. Accordingly, it is an aspect of the present disclosure toutilize such historical MS signal location data for enhancing thecorrectness and/or confidence of certain location hypotheses as will bedescribed in detail in other sections below.

Data Representations for the Location Signature Data Base

There are four fundamental entity types (or object classes in an objectoriented programming paradigm) utilized in the location signature database 1320. Briefly, these data entities are described in the items(24.1) through (24.4) that follow:

(24.1) (verified) location signatures: Each such (verified) locationsignature describes the wireless signal characteristic measurementsbetween a given base station (e.g., BS 122 or LBS 152) and an MS 140 ata (verified or known) location associated with the (verified) locationsignature. That is, a verified location signature corresponds to alocation whose coordinates such as latitude-longitude coordinates areknown, while simply a location signature may have a known or unknownlocation corresponding with it. Note that the term (verified) locationsignature is also denoted by the abbreviation, “(verified) loc sig”hereinbelow;(24.2) (verified) location signature clusters: Each such (verified)location signature cluster includes a collection of (verified) locationsignatures corresponding to all the location signatures between a targetMS 140 at a (possibly verified) presumed substantially stationarylocation and each BS (e.g., 122 or 152) from which the target MS 140 candetect the BS's pilot channel regardless of the classification of the BSin the target MS (i.e., for CDMA, regardless of whether a BS is in theMS's active, candidate or remaining base station sets, as one skilled inthe art will understand). Note that for simplicity here, it is presumedthat each location signature cluster has a single fixed primary basestation to which the target MS 140 synchronizes or obtains its timing;(24.3) “composite location objects (or entities)”: Each such entity is amore general entity than the verified location signature cluster. Anobject of this type is a collection of (verified) location signaturesthat are associated with the same MS 140 at substantially the samelocation at the same time and each such loc sig is associated with adifferent base station. However, there is no requirement that a loc sigfrom each BS 122 for which the MS 140 can detect the BS's pilot channelis included in the “composite location object (or entity)”; and(24.4) MS location estimation data that includes MS location estimatesoutput by one or more MS location estimating first order models 1224,such MS location estimate data is described in detail hereinbelow.

It is important to note that a loc sig is, in one embodiment, aninstance of the data structure containing the signal characteristicmeasurements output by the signal filtering and normalizing subsystemalso denoted as the signal processing subsystem 1220 describing thesignals between: (i) a specific base station 122 (BS) and (ii) a mobilestation 140 (MS), wherein the BS's location is known and the MS'slocation is assumed to be substantially constant (during a 2-5 secondinterval in one embodiment of the novel wireless system and/or methoddisclosed herein), during communication with the MS 140 for obtaining asingle instance of loc sig data, although the MS location may or may notbe known. Further, for notational purposes, the BS 122 and the MS 140for a loc sig hereinafter will be denoted the “BS associated with theloc sig”, and the “MS associated with the loc sig” respectively.Moreover, the location of the MS 140 at the time the loc sig data isobtained will be denoted the “location associated with the loc sig”(this location possibly being unknown).

Loc Sig Description

In particular, for each (verified) loc sig includes the following:

-   (25.1) MS_type: the make and model of the target MS 140 associated    with a location signature instantiation; note that the type of MS    140 can also be derived from this entry; e.g., whether MS 140 is a    handset MS, car-set MS, or an MS for location only. Note as an    aside, for at least CDMA, the type of MS 140 provides information as    to the number of fingers that may be measured by the MS, as one    skilled in the will appreciate.-   (25.2) BS_id: an identification of the base station 122 (or,    location base station 152) communicating with the target MS;-   (25.3) MS_loc: a representation of a geographic location (e.g.,    latitude-longitude) or area representing a verified/known MS    location where signal characteristics between the associated    (location) base station and MS 140 were received. That is, if the    “verified_flag” attribute (discussed below) is TRUE, then this    attribute includes an estimated location of the target MS. If    verified_flag is FALSE, then this attribute has a value indicating    “location unknown”.    -   Note “MS_loc” may include the following two subfields: an area        within which the target MS is presumed to be, and a point        location (e.g., a latitude and longitude pair) where the target        MS is presumed to be (in one embodiment this is the centroid of        the area);-   (25.4) verified_flag: a flag for determining whether the loc sig has    been verified; i.e., the value here is TRUE iff a location of MS_loc    has been verified, FALSE otherwise. Note, if this field is TRUE    (i.e., the loc sig is verified), then the base station identified by    BS_id is the current primary base station for the target MS;-   (25.5) confidence: a value indicating how consistent this loc sig is    with other loc sigs in the location signature data base 1320; the    value for this entry is in the range [0, 1] with 0 corresponding to    the lowest (i.e., no) confidence and 1 corresponding to the highest    confidence. That is, the confidence factor is used for determining    how consistent the loc sig is with other “similar” verified loc sigs    in the location signature data base 1320, wherein the greater the    confidence value, the better the consistency with other loc sigs in    the data base. Note that similarity in this context may be    operationalized by at least designating a geographic proximity of a    loc sig in which to determine if it is similar to other loc sigs in    this designated geographic proximity and/or area type (e.g.,    transmission area type as elsewhere herein). Thus, environmental    characteristics may also be used in determining similarities such    as: similar time of occurrence (e.g., of day, and/or of month),    similar weather (e.g., snowing, raining, etc.). Note, these latter    characteristics are different from the notion of geographic    proximity since proximity may be only a distance measurement about a    location. Note also that a loc sig having a confidence factor value    below a predetermined threshold may not be used in evaluating MS    location hypotheses generated by the FOMs 1224.-   (25.6) timestamp: the time and date the loc sig was received by the    associated base station of BS_id;-   (25.7) signal topography characteristics: In one embodiment, the    signal topography characteristics retained can be represented as    characteristics of at least a two-dimensional generated surface.    That is, such a surface is generated by the signal processing    subsystem 1220 from signal characteristics accumulated over (a    relatively short) time interval. For example, in the two-dimensional    surface case, the dimensions for the generated surface may be, for    example, signal strength and time delay. That is, the accumulations    over a brief time interval of signal characteristic measurements    between the BS 122 and the MS 140 (associated with the loc sig) may    be classified according to the two signal characteristic dimensions    (e.g., signal strength and corresponding time delay). That is, by    sampling the signal characteristics and classifying the samples    according to a mesh of discrete cells or bins, wherein each cell    corresponds to a different range of signal strengths and time delays    a tally of the number of samples falling in the range of each cell    can be maintained. Accordingly, for each cell, its corresponding    tally may be interpreted as height of the cell, so that when the    heights of all cells are considered, an undulating or mountainous    surface is provided. In particular, for a cell mesh of appropriate    fineness, the “mountainous surface”, is believed to, under most    circumstances, provide a contour that is substantially unique to the    location of the target MS 140. Note that in one embodiment, the    signal samples are typically obtained throughout a predetermined    signal sampling time interval of 2-5 seconds as is discussed    elsewhere in this specification. In particular, the signal    topography characteristics retained for a loc sig include certain    topographical characteristics of such a generated mountainous    surface. For example, each loc sig may include: for each local    maximum (of the loc sig surface) above a predetermined noise ceiling    threshold, the (signal strength, time delay) coordinates of the cell    of the local maximum and the corresponding height of the local    maximum. Additionally, certain gradients may also be included for    characterizing the “steepness” of the surface mountains. Moreover,    note that in some embodiments, a frequency may also be associated    with each local maximum. Thus, the data retained for each selected    local maximum can include a quadruple of signal strength, time    delay, height and frequency. Further note that the data types here    may vary. However, for simplicity, in parts of the description of    loc sig processing related to the signal characteristics here, it is    assumed that the signal characteristic topography data structure    here is a vector;-   (25.8) quality_obj: signal quality (or error) measurements, e.g.,    Eb/No values, as one skilled in the art will understand;-   (25.9) noise_ceiling: noise ceiling values used in the initial    filtering of noise from the signal topography characteristics as    provided by the signal processing subsystem 1220;-   (25.10) power_level: power levels of the base station (e.g., 122 or    152) and MS 140 for the signal measurements;-   (25.11) timing_error: an estimated (or maximum) timing error between    the present (associated) BS (e.g., an infrastructure base station    122 or a location base station 152) detecting the target MS 140 and    the current primary BS 122 for the target MS 140. Note that if the    BS 122 associated with the loc sig is the primary base station, then    the value here will be zero;-   (25.12) cluster_ptr: a pointer to the location signature composite    entity to which this loc sig belongs.-   (25.13) repeatable: TRUE iff the loc sig is “repeatable” (as    described hereinafter), FALSE otherwise. Note that each verified loc    sig is designated as either “repeatable” or “random”. A loc sig is    repeatable if the (verified/known) location associated with the loc    sig is such that signal characteristic measurements between the    associated BS 122 and this MS can be either replaced at periodic    time intervals, or updated substantially on demand by most recent    signal characteristic measurements between the associated base    station and the associated MS 140 (or a comparable MS) at the    verified/known location. Repeatable loc sigs may be, for example,    provided by stationary or fixed location MSs 140 (e.g., fixed    location transceivers) distributed within certain areas of a    geographical region serviced by the location center 142 for    providing MS location estimates. That is, it is an aspect of the    present disclosure that each such stationary MS 140 can be contacted    by the location center 142 (via the base stations of the wireless    infrastructure) at substantially any time for providing a new    collection (i.e., cluster) of wireless signal characteristics to be    associated with the verified location for the transceiver.    Alternatively, repeatable loc sigs may be obtained by, for example,    obtaining location signal measurements manually from workers who    regularly traverse a predetermined route through some portion of the    radio coverage area; i.e., postal workers.    -   A loc sig is random if the loc sig is not repeatable. Random loc        sigs are obtained, for example, from verifying a previously        unknown target MS location once the MS 140 has been located.        Such verifications may be accomplished by, for example, a        vehicle having one or more location verifying devices such as a        GPS receiver and/or a manual location input capability becoming        sufficiently close to the located target MS 140 so that the        location of the vehicle may be associated with the wireless        signal characteristics of the MS 140. Vehicles having such        location detection devices may include: (a) vehicles that travel        to locations that are primarily for another purpose than to        verify loc sigs, e.g., police cars, ambulances, fire trucks,        rescue units, courier services and taxis; and/or (b) vehicles        whose primary purpose is to verify loc sigs; e.g., location        signal calibration vehicles. Additionally, vehicles having both        wireless transceivers and location verifying devices may provide        the location center 142 with random loc sigs. Note, a repeatable        loc sig may become a random loc sig if an MS 140 at the location        associated with the loc sig becomes undetectable such as, for        example, when the MS 140 is removed from its verified location        and therefore the loc sig for the location can not be readily        updated.

Additionally, note that at least in one embodiment of the signaltopography characteristics (25.7) above, such a first surface may begenerated for the (forward) signals from the base station 122 to thetarget MS 140 and a second such surface may be generated for (oralternatively, the first surface may be enhanced by increasing itsdimensionality with) the signals from the MS 140 to the base station 122(denoted the reverse signals).

Additionally, in some embodiments the location hypothesis may include anestimated error as a measurement of perceived accuracy in addition to oras a substitute for the confidence field discussed hereinabove.Moreover, location hypotheses may also include a text field forproviding a reason for the values of one or more of the locationhypothesis fields. For example, this text field may provide a reason asto why the confidence value is low, or provide an indication that thewireless signal measurements used had a low signal to noise ratio.

Loc sigs have the following functions or object methods associatedtherewith:

-   (26.1) A “normalization” method for normalizing loc sig data    according to the associated MS 140 and/or BS 122 signal processing    and generating characteristics. That is, the signal processing    subsystem 1220, one embodiment being described in the PCT patent    application PCT/US97/15933, titled, “Wireless Location Using A    Plurality of Commercial Network Infrastructures,” by F. W. LeBlanc    and the present inventors, filed Sep. 8, 1997 (which has a U.S.    national filing that is now U.S. Pat. No. 6,236,365, filed Jul. 8,    1999, note, both PCT/US97/15933 and U.S. Pat. No. 6,236,365 are    incorporated fully by reference herein) provides (methods for loc    sig objects) for “normalizing” each loc sig so that variations in    signal characteristics resulting from variations in (for example) MS    signal processing and generating characteristics of different types    of MS's may be deduced In particular, since wireless network    designers are typically designing networks for effective use of hand    set MS's 140 having a substantially common minimum set of    performance characteristics, the normalization methods provided here    transform the loc sig data so that it appears as though the loc sig    was provided by a common hand set MS 140. However, other methods may    also be provided to “normalize” a loc sig so that it may be compared    with loc sigs obtained from other types of MS's as well. Note that    such normalization techniques include, for example, interpolating    and extrapolating according to power levels so that loc sigs may be    normalized to the same power level for, e.g., comparison purposes.    -   Normalization for the BS 122 associated with a loc sig is        similar to the normalization for MS signal processing and        generating characteristics. Just as with the MS normalization,        the signal processing subsystem 1220 provides a loc sig method        for “normalizing” loc sigs according to base station signal        processing and generating characteristics.    -   Note, however, loc sigs stored in the location signature data        base 1320 are NOT “normalized” according to either MS or BS        signal processing and generating characteristics. That is, “raw”        values of the wireless signal characteristics are stored with        each loc sig in the location signature data base 1320.-   (26.2) A method for determining the “area type” corresponding to the    signal transmission characteristics of the area(s) between the    associated BS 122 and the associated MS 140 location for the loc    sig. Note, such an area type may be designated by, for example, the    techniques for determining transmission area types as described    hereinabove.-   (26.3) Other methods are contemplated for determining additional    environmental characteristics of the geographical area between the    associated BS 122 and the associated MS 140 location for the loc    sig; e.g., a noise value indicating the amount of noise likely in    such an area.

Referring now to the composite location objects and verified locationsignature clusters of (24.3) and (24.2) respectively, the followinginformation is contained in these aggregation objects:

-   (27.1.1) an identification of the BS 122 designated as the primary    base station for communicating with the target MS 140;-   (27.1.2) a reference to each loc sig in the location signature data    base 1320 that is for the same MS location at substantially the same    time with the primary BS as identified in (27.1);-   (27.1.3) an identification of each base station (e.g., 122 and 152)    that can be detected by the MS 140 at the time the location signal    measurements are obtained. Note that in one embodiment, each    composite location object includes a bit string having a    corresponding bit for each base station, wherein a “1” for such a    bit indicates that the corresponding base station was identified by    the MS, and a “0” indicates that the base station was not    identified. In an alternative embodiment, additional location signal    measurements may also be included from other non-primary base    stations. For example, the target MS 140 may communicate with other    base stations than it's primary base station. However, since the    timing for the MS 140 is typically derived from it's primary base    station and since timing synchronization between base stations is    not exact (e.g., in the case of CDMA, timing variations may be plus    or minus 1 microsecond) at least some of the location signal    measurements may be less reliable that the measurements from the    primary base station, unless a forced hand-off technique is used to    eliminate system timing errors among relevant base stations;-   (27.1.4) a completeness designation that indicates whether any loc    sigs for the composite location object have been removed from (or    invalidated in) the location signature data base 1320.

Note, a verified composite location object is designated as “incomplete”if a loc sig initially referenced by the verified composite locationobject is deleted from the location signature data base 1320 (e.g.,because of a confidence that is too low). Further note that if all locsigs for a composite location object are deleted, then the compositeobject is also deleted from the location signature data base 1320. Alsonote that common fields between loc sigs referenced by the samecomposite location object may be provided in the composite locationobject only (e.g., timestamp, etc.).

Accordingly, a composite location object that is complete (i.e., notincomplete) is a verified location signature cluster as described in(24.2).

Location Center Architecture

Overview of Location Center Functional Components

FIG. 5 presents a high level diagram of the location center 142 and thelocation engine 139 in the context of the infrastructure for anembodiment of the novel wireless location system and/or method disclosedherein.

It is important to note that the architecture for the location center142 and the location engine 139 is designed for extensibility andflexibility so that MS 140 location accuracy and reliability may beenhanced as further location data become available and as enhanced MSlocation techniques become available. In addressing the design goals ofextensibility and flexibility, the high level architecture forgenerating and processing MS location estimates may be considered asdivided into the following high level functional groups describedhereinbelow.

Low Level Wireless Signal Processing Subsystem for Receiving andConditioning Wireless Signal Measurements

A first functional group of location engine 139 modules is forperforming signal processing and filtering of MS location signal datareceived from a conventional wireless (e.g., CDMA) infrastructure, asdiscussed in the steps (23.1) and (23.2) above. This group is denotedthe signal processing subsystem 1220 herein. One embodiment of such asubsystem is described in the PCT patent application titled, “WirelessLocation Using A Plurality of Commercial Network Infrastructures,” by F.W. LeBlanc and the present inventors.

Initial Location Estimators: First Order Models

A second functional group of location engine 139 modules is forgenerating various target MS 140 location initial estimates, asdescribed in step (23.3). Accordingly, the modules here use inputprovided by the signal processing subsystem 1220. This second functionalgroup includes one or more signal analysis modules or models, eachhereinafter denoted as a first order model 1224 (FOM), for generatinglocation hypotheses for a target MS 140 to be located. Note that it isintended that each such FOM 1224 use a different technique fordetermining a location area estimate for the target MS 140. A briefdescription of some types of first order models is provided immediatelybelow. Note that FIG. 8 illustrates another, more detailed view of thelocation system 142. In particular, this figure illustrates some of theFOMs 1224 contemplated by the present disclosure, and additionallyillustrates the primary communications with other modules of thelocation system 142. However, it is important to note that the presentdisclosure is not limited to the FOMs 1224 shown and discussed herein.That is, it is a primary aspect of the present disclosure to easilyincorporate FOMs using other signal processing and/or computationallocation estimating techniques than those presented herein. Further,note that each FOM type may have a plurality of its models incorporatedinto an embodiment of a novel wireless location system according to thepresent disclosure.

For example, (as will be described in further detail below), one suchtype of model or FOM 1224 (hereinafter models of this type are referredto as “distance models”) may be based on a range or distance computationand/or on a base station signal reception angle determination betweenthe target MS 140 from each of one or more base stations. Basically,such distance models 1224 determine a location estimate of the target MS140 by determining a distance offset from each of one or more basestations 122, possibly in a particular direction from each (some of) thebase stations, so that an intersection of each area locus defined by thebase station offsets may provide an estimate of the location of thetarget MS. Distance model FOMs 1224 may compute such offsets based on:

-   -   (a) signal timing measurements between the target mobile station        140 and one or more base stations 122; e.g., timing measurements        such as time difference of arrival (TDOA), or time of arrival        (TOA). Note that both forward and reverse signal path timing        measurements may be utilized;    -   (b) signal strength measurements (e.g., relative to power        control settings of the MS 140 and/or one or more BS 122);        and/or    -   (c) signal angle of arrival measurements, or ranges thereof, at        one or more base stations 122 (such angles and/or angular ranges        provided by, e.g., base station antenna sectors having angular        ranges of 120° or 60°, or, so called “SMART antennas” with        variable angular transmission ranges of 2° to 120°).        Accordingly, a distance model may utilize triangulation or        trilateration to compute a location hypothesis having either an        area location or a point location for an estimate of the target        MS 140. Additionally, in some embodiments location hypothesis        may include an estimated error.

In one embodiment, such a distance model may perform the followingsteps:

-   -   (a) Determines a minimum distance between the target MS and each        BS using TOA, TDOA, signal strength on both forward and reverse        paths;    -   (b) Generates an estimated error;    -   (c) Outputs a location hypothesis for estimating a location of a        MS: each such hypothesis having: (a) one or more (nested)        location area estimates for the MS, each location estimate        having a confidence value (e.g., provided using the estimated        error) indicating a perceived accuracy, and (b) a reason for        both the location estimate (e.g., substantial multipath, etc)        and the confidence.

Another type of FOM 1224 is a statistically based first order model1224, wherein a statistical technique, such as regression techniques(e.g., least squares, partial least squares, principle decomposition),or e.g., Bollenger Bands (e.g., for computing minimum and maximum basestation offsets). In general, models of this type output locationhypotheses that are determined by performing one or more statisticaltechniques or comparisons between the verified location signatures inlocation signature data base 1320, and the wireless signal measurementsfrom a target MS. Models of this type are also referred to hereinafteras a “stochastic signal (first order) model” or a “stochastic FOM” or a“statistical model.”

In one embodiment, such a stochastic signal model may output locationhypotheses determined by one or more statistical comparisons with locsigs in the Location Signature database 1320 (e.g., comparing MSlocation signals with verified signal characteristics for predeterminedgeographical areas).

Still another type of FOM 1224 is an adaptive learning model, such as anartificial neural net or a genetic algorithm, wherein the FOM may betrained to recognize or associate each of a plurality of locations witha corresponding set of signal characteristics for communications betweenthe target MS 140 (at the location) and the base stations 122. Moreover,typically such a FOM is expected to accurately interpolate/extrapolatetarget MS 140 location estimates from a set of signal characteristicsfrom an unknown target MS 140 location. Models of this type are alsoreferred to hereinafter variously as “artificial neural net models” or“neural net models” or “trainable models” or “learning models.” Notethat a related type of FOM 1224 is based on pattern recognition. TheseFOMs can recognize patterns in the signal characteristics ofcommunications between the target MS 140 (at the location) and the basestations 122 and thereby estimate a location area of the target MS.However, such FOMs may not be trainable.

In one embodiment, an adaptive learning model such as a model based onan artificial neural network may determine an MS 140 location estimateusing base station IDs, data on signal-to-noise, other signal data(e.g., a number of signal characteristics including, e.g., all CDMAfingers). Moreover, the output from such a model may include: a latitudeand longitude for a center of a circle having radius R (R may be aninput to such an artificial neural network), and is in the output formatof the distance model(s).

Yet another type of FOM 1224 can be based on a collection of dispersedlow power, low cost fixed location wireless transceivers (also denoted“location base stations 152” hereinabove) that are provided fordetecting a target MS 140 in areas where, e.g., there is insufficientbase station 122 infrastructure coverage for providing a desired levelof MS 140 location accuracy. For example, it may uneconomical to providehigh traffic wireless voice coverage of a typical wireless base station122 in a nature preserve or at a fair ground that is only populated afew days out of the year. However, if such low cost location basestations 152 can be directed to activate and deactivate via thedirection of a FOM 1224 of the present type, then these location basestations can be used to both locate a target MS 140 and also provideindications of where the target MS is not. For example, if there arelocation base stations 152 populating an area where the target MS 140 ispresumed to be, then by activating these location base stations 152,evidence may be obtained as to whether or not the target MS is actuallyin the area; e.g., if the target MS 140 is detected by a location basestation 152, then a corresponding location hypothesis having a locationestimate corresponding to the coverage area of the location base stationmay have a very high confidence value. Alternatively, if the target MS140 is not detected by a location base station 152, then a correspondinglocation hypothesis having a location estimate corresponding to thecoverage area of the location base station may have a very lowconfidence value. Models of this type are referred to hereinafter as“location base station models.”

In one embodiment, such a location base station model may perform thefollowing steps:

-   -   (a) If an input is received then the target MS 140 is detected        by a location base station 152 (i.e., a LBS being a unit having        a reduced power BS and a MS).    -   (b) If an input is obtained, then the output is a hypothesis        data structure having a small area of the highest confidence.    -   (c) If no input is received from a LBS then a hypothesis having        an area with highest negative confidence is output.

Yet another type of FOM 1224 can be based on input from a mobile basestation 148, wherein location hypotheses may be generated from target MS140 location data received from the mobile base station 148. In oneembodiment, such a mobile base station model may provide output similarto the distance FOM 1224 described hereinabove.

Still other types of FOM 1224 can be based on various techniques forrecognizing wireless signal measurement patterns and associatingparticular patterns with locations in the coverage area 120. Forexample, artificial neural networks or other learning models can be usedas the basis for various FOMs.

Note that the FOM types mentioned here as well as other FOM types arediscussed in detail hereinbelow. Moreover, it is important to keep inmind that a novel aspect of the present disclosure is the simultaneoususe or activation of a potentially large number of such first ordermodels 1224, wherein such FOMs are not limited to those describedherein. Thus, the present disclosure provides a framework forincorporating MS location estimators to be subsequently provided as newFOMs in a straightforward manner. For example, a FOM 1224 based onwireless signal time delay measurements from a distributed antennasystem 168 for wireless communication may be incorporated into anembodiment of a novel wireless location system according to the presentdisclosure for locating a target MS 140 in an enclosed area serviced bythe distributed antenna system (such a FOM is more fully described inthe U.S. Pat. No. 6,236,365 filed Jul. 8, 1999 which is incorporatedfully by reference herein). Accordingly, by using such a distributedantenna FOM 1224 (FIG. 8(1)), an embodiment of a novel wireless locationsystem and/or method disclosed herein may determine the floor of amulti-story building from which a target MS is transmitting. Thus, MSs140 can be located in three dimensions using such a distributed antennaFOM 1224.

In one embodiment, such a distributed antenna model may perform thefollowing steps:

(a) Receives input only from a distributed antenna system.

(b) If an input is received, then the output is a lat-long and height ofhighest confidence.

Additionally, FOMs 1224 for detecting certain registration changeswithin, for example, a public switched telephone network 124 can also beused for locating a target MS 140. For example, for some MSs 140 theremay be an associated or dedicated device for each such MS that allowsthe MS to function as a cordless phone to a line based telephone networkwhen the device detects that the MS is within signaling range. In oneuse of such a device (also denoted herein as a “home base station”), thedevice registers with a home location register of the public switchedtelephone network 124 when there is a status change such as from notdetecting the corresponding MS to detecting the MS, or visa versa, asone skilled in the art will understand. Accordingly, by providing a FOM1224 (denoted the “Home Base Station First Order Model” in FIG. 8(1))that accesses the MS status in the home location register, the locationengine 139 can determine whether the MS is within signaling range of thehome base station or not, and generate location hypotheses accordingly.

In one embodiment, such a home base station model may perform thefollowing steps:

-   -   (a) Receives an input only from the Public Telephone Switching        Network.    -   (b) If an input is received then the target MS 140 is detected        by a home base station associated with the target MS.    -   (c) If an input is obtained, then the output is a hypothesis        data structure having a small area of the highest confidence.    -   (d) If no input and there is a home base station then a        hypothesis having a negative area is of highest confidence is        output.

Moreover, other FOMs based on, for example, chaos theory and/or fractaltheory are also within the scope of the present disclosure.

It is important to note the following aspects of the present disclosurerelating to FOMs 1224:

-   (28.1) Each such first order model 1224 may be relatively easily    incorporated into and/or removed from an embodiment of a novel    wireless location system according to the present disclosure. For    example, assuming that the signal processing subsystem 1220 provides    uniform input interface to the FOMs, and there is a uniform FOM    output interface, it is believed that a large majority (if not    substantially all) viable MS location estimation strategies may be    accommodated. Thus, it is straightforward to add or delete such FOMs    1224.-   (28.2) Each such first order model 1224 may be relatively simple and    still provide significant MS 140 locating functionality and    predictability. For example, much of what is believed to be common    or generic MS location processing has been coalesced into, for    example: a location hypothesis evaluation subsystem, denoted the    hypotheses evaluator 1228 and described immediately below. Thus, an    embodiment of a novel wireless location system according to the    present disclosure is modular and extensible such that, for example,    (and importantly) different first order models 1224 may be utilized    depending on the signal transmission characteristics of the    geographic region serviced by an embodiment of the present    disclosure. Thus, a simple configuration of an embodiment of a novel    wireless location system and/or method disclosed herein may have a    small number of FOMs 1224 for a simple wireless signal environment    (e.g., flat terrain, no urban canyons and low population density).    Alternatively, for complex wireless signal environments such as in    cities like San Francisco, Tokyo or New York, a large number of FOMs    1224 may be simultaneously utilized for generating MS location    hypotheses.    An Introduction to an Evaluator for Location Hypotheses: Hypothesis    Evaluator

A third functional group of location engine 139 modules evaluateslocation hypotheses output by the first order models 1224 and therebyprovides a “most likely” target MS location estimate. The modules forthis functional group are collectively denoted the hypothesis evaluator1228.

Hypothesis Evaluator Introduction

A primary purpose of the hypothesis evaluator 1228 is to mitigateconflicts and ambiguities related to location hypotheses output by thefirst order models 1224 and thereby output a “most likely” estimate ofan MS for which there is a request for it to be located. In providingthis capability, there are various related embodiments of the hypothesisevaluator that are within the scope of the present disclosure. Sinceeach location hypothesis includes both an MS location area estimate anda corresponding confidence value indicating a perceived confidence orlikelihood of the target MS being within the corresponding location areaestimate, there is a monotonic relationship between MS location areaestimates and confidence values. That is, by increasing an MS locationarea estimate, the corresponding confidence value may also be increased(in an extreme case, the location area estimate could be the entirecoverage area 120 and thus the confidence value may likely correspond tothe highest level of certainty; i.e., +1.0). Accordingly, given a targetMS location area estimate (of a location hypothesis), an adjustment toits accuracy may be performed by adjusting the MS location area estimateand/or the corresponding confidence value. Thus, if the confidence valueis, for example, excessively low then the area estimate may be increasedas a technique for increasing the confidence value. Alternatively, ifthe estimated area is excessively large, and there is flexibility in thecorresponding confidence value, then the estimated area may be decreasedand the confidence value also decreased. Thus, if at some point in theprocessing of a location hypothesis, if the location hypothesis isjudged to be more (less) accurate than initially determined, then (i)the confidence value of the location hypothesis can be increased(decreased), and/or (ii) the MS location area estimate can be decreased(increased).

In a first class of embodiments, the hypothesis evaluator 1228 evaluateslocation hypotheses and adjusts or modifies only their confidence valuesfor MS location area estimates and subsequently uses these MS locationestimates with the adjusted confidence values for determining a “mostlikely” MS location estimate for outputting. Accordingly, the MSlocation area estimates are not substantially modified. Alternatively,in a second class of embodiments for the hypothesis evaluator 1228, MSlocation area estimates can be adjusted while confidence values remainsubstantially fixed. Of course, hybrids between the first twoembodiments can also be provided. Note that the present embodimentprovided herein adjusts both the areas and the confidence values.

More particularly, the hypothesis evaluator 1228 may perform any or mostof the following tasks:

-   (30.1) it utilizes environmental information to improve and    reconcile location hypotheses supplied by the first order models    1224. A basic premise in this context is that the accuracy of the    individual first order models may be affected by various    environmental factors such as, for example, the season of the year,    the time of day, the weather conditions, the presence of buildings,    base station failures, etc.;-   (30.2) it enhances the accuracy of an initial location hypothesis    generated by a FOM by using the initial location hypothesis as,    essentially, a query or index into the location signature data base    1320 for obtaining a corresponding enhanced location hypothesis,    wherein the enhanced location hypothesis has both an adjusted target    MS location area estimate and an adjusted confidence based on past    performance of the FOM in the location service surrounding the    target MS location estimate of the initial location hypothesis;-   (30.3) it determines how well the associated signal characteristics    used for locating a target MS compare with particular verified loc    sigs stored in the location signature data base 1320 (see the    location signature data base section for further discussion    regarding this aspect of the invention). That is, for a given    location hypothesis, verified loc sigs (which were previously    obtained from one or more verified locations of one or more MS's)    are retrieved for an area corresponding to the location area    estimate of the location hypothesis, and the signal characteristics    of these verified loc sigs are compared with the signal    characteristics used to generate the location hypothesis for    determining their similarities and subsequently an adjustment to the    confidence of the location hypothesis (and/or the size of the    location area estimate);-   (30.4) the hypothesis evaluator 1228 determines if (or how well)    such location hypotheses are consistent with well known physical    constraints such as the laws of physics. For example, if the    difference between a previous (most likely) location estimate of a    target MS and a location estimate by a current location hypothesis    requires the MS to:    -   (a1) move at an unreasonably high rate of speed (e.g., 200 mph),        or    -   (b1) move at an unreasonably high rate of speed for an area        (e.g., 80 mph in a corn patch), or    -   (c1) make unreasonably sharp velocity changes (e.g., from 60 mph        in one direction to 60 mph in the opposite direction in 4 sec),        then the confidence in the current Location Hypothesis is likely        to be reduced.-    Alternatively, if for example, the difference between a previous    location estimate of a target MS and a current location hypothesis    indicates that the MS is:    -   (a2) moving at an appropriate velocity for the area being        traversed, or    -   (b2) moving along an established path (e.g., a freeway),-    then the confidence in the current location hypothesis may be    increased.-   (30.5) the hypothesis evaluator 1228 determines consistencies and    inconsistencies between location hypotheses obtained from different    first order models. For example, if two such location hypotheses,    for substantially the same timestamp, have estimated location areas    where the target MS is likely to be and these areas substantially    overlap, then the confidence in both such location hypotheses may be    increased. Additionally, note that a velocity of an MS may be    determined (via deltas of successive location hypotheses from one or    more first order models) even when there is low confidence in the    location estimates for the MS, since such deltas may, in some cases,    be more reliable than the actual target MS location estimates;-   (30.6) the hypothesis evaluator 1228 determines new (more accurate)    location hypotheses from other location hypotheses. For example,    this module may generate new hypotheses from currently active ones    by decomposing a location hypothesis having a target MS location    estimate intersecting two radically different area types.    Additionally, this module may generate location hypotheses    indicating areas of poor reception; and-   (30.7) the hypothesis evaluator 1228 determines and outputs a most    likely location hypothesis for a target MS. Note that the hypothesis    evaluator may accomplish the above tasks, (30.1)-(30.7), by    employing various data processing tools including, but not limited    to, fuzzy mathematics, genetic algorithms, neural networks, expert    systems and/or blackboard systems.

Note that, as can be seen in FIGS. 6 and 7, the hypothesis evaluator1228 includes the following four high level modules for processingoutput location hypotheses from the first order models 1224: a contextadjuster 1326, a hypothesis analyzer 1332, an MS status repository 1338and a most likelihood estimator 1334. These four modules are brieflydescribed hereinbelow.

Context Adjuster Introduction.

The context adjuster 1326 module enhances both the comparability andpredictability of the location hypotheses output by the first ordermodels 1224. In particular, this module modifies location hypothesesreceived from the FOMs 1224 so that the resulting location hypothesesoutput by the context adjuster 1326 may be further processed uniformlyand substantially without concern as to differences in accuracy betweenthe first order models from which location hypotheses originate. Inproviding this capability, the context adjuster 1326 may adjust ormodify various fields of the input location hypotheses. In particular,fields giving target MS 140 location estimates and/or confidence valuesfor such estimates may be modified by the context adjuster 1326.Further, this module may determine those factors that are perceived toimpact the perceived accuracy (e.g., confidence) of the locationhypotheses: (a) differently between FOMs, and/or (b) with substantialeffect. For instance, environmental characteristics may be taken intoaccount here, such as time of day, season, month, weather, geographicalarea categorizations (e.g., dense urban, urban, suburban, rural,mountain, etc.), area subcategorizations (e.g., heavily treed, hilly,high traffic area, etc.). A detailed description of one embodiment ofthis module is provided in APPENDIX D hereinbelow. Note that, theembodiment described herein is simplified for illustration purposes suchthat only the geographical area categorizations are utilized inadjusting (i.e., modifying) location hypotheses. But, it is an importantaspect of the present disclosure that various categorizations, such asthose mentioned immediately above, may be used for adjusting thelocation hypotheses. That is, categories such as, for example:

-   -   (a) urban, hilly, high traffic at 5 pm, or    -   (b) rural, flat, heavy tree foliage density in summer may be        utilized as one skilled in the art will understand from the        descriptions contained hereinbelow.

Accordingly, the present disclosure is not limited to the factorsexplicitly mentioned here. That is, it is an aspect of the presentdisclosure to be extensible so that other environmental factors of thecoverage area 120 affecting the accuracy of location hypotheses may alsobe incorporated into the context adjuster 1326.

It is also an important and novel aspect of the context adjuster 1326that the methods for adjusting location hypotheses provided in thismodule may be generalized and thereby also utilized with multiplehypothesis computational architectures related to various applicationswherein a terrain, surface, volume or other “geometric” interpretation(e.g., a metric space of statistical samples) may be placed on a largebody of stored application data for relating hypothesized data toverified data. Moreover, it is important to note that various techniquesfor “visualizing data” may provide such a geometric interpretation.Thus, the methods herein may be utilized in applications such as:

-   -   (a) sonar, radar, x-ray or infrared identification of objects        such as occurs in robotic navigation, medical image analysis,        geological, and radar imaging.

More generally, the novel computational paradigm of the context adjuster1326 may be utilized in a number of applications wherein there is alarge body of archived information providing verified or actualapplication process data related to the past performance of theapplication process.

It is worth mentioning that the computational paradigm used in thecontext adjuster 1326 is a hybrid of a hypothesis adjuster and a database query mechanism. For example, the context adjuster 1326 uses aninput (location) hypothesis both as an hypothesis and as a data basequery or index into the location signature data base 1320 forconstructing a related but more accurate location hypothesis.Accordingly, substantial advantages are provided by this hybridarchitecture, such as the following two advantages.

As a first advantage, the context adjuster 1326 reduces the likelihoodthat a feedback mechanism is necessary to the initial hypothesisgenerators (i.e., FOMs 1224) for periodically adjusting defaultevaluations of the goodness or confidence in the hypotheses generated.That is, since each hypothesis generated is, in effect, an index into adata base or archive of verified application (e.g., location) data, thecontext adjuster 1326, in turn, generates new corresponding hypothesesbased on the actual or verified data retrieved from an archival database. Thus, as a result, this architecture tends to separate thecomputations of the initial hypothesis generators (e.g., the FOMs 1224in the present MS location application) from any further processing andthereby provide a more modular, maintainable and flexible computationalsystem.

As a second advantage, the context adjuster 1326 tends to createhypotheses that are more accurate than the hypotheses generated by theinitial hypotheses generators. That is, for each hypothesis, H, providedby one of the initial hypothesis generators, G (e.g., a FOM 1224), acorresponding enhanced hypothesis, provided by the context adjuster1326, is generated by mapping the past performance of G into thearchived verified application data (as will be discussed in detailhereinbelow). In particular, the context adjuster hypothesis generationis based on the archived verified (or known) performance applicationdata that is related to both G and H. For example, in the presentwireless location application, if a FOM 1224, G, substantiallyconsistently generates, in a particular geographical area, locationhypotheses that are biased approximately 1000 feet north of the actualverified MS 140 location, then the context adjuster 1326 can generatecorresponding hypotheses without this bias. Thus, the context adjuster1326 tends to filter out inaccuracies in the initially generatedhypotheses.

Therefore in a multiple hypothesis architecture where typically thegenerated hypotheses may be evaluated and/or combined for providing a“most likely” result, it is believed that a plurality of relativelysimple (and possibly inexact) initial hypothesis generators may be usedin conjunction with the hybrid computational paradigm represented by thecontext adjuster 1326 for providing enhanced hypotheses withsubstantially greater accuracy.

Additionally, note that this hybrid paradigm applies to other domainsthat are not geographically based. For instance, this hybrid paradigmapplies to many prediction and/or diagnostic applications for which:

(a) the application data and the application are dependent on a numberof parameters whose values characterize the range of outputs for theapplication. That is, there is a set of parameters, p₁, p₂, p₃, . . . ,p_(N) from which a parameter space p₁×p₂×p₃× . . . ×p_(N) is derivedwhose points characterize the actual and estimated (or predicted)outcomes. As examples, in the MS location system, p₁=latitude andp₂=longitude;

(b) there is historical data from which points for the parameter space,p₁×p₂×p₃× . . . ×p_(N) can be obtained, wherein this data relates to (orindicates) the performance of the application, and the points obtainedfrom this data are relatively dense in the space (at least around thelikely future actual outcomes that the application is expected topredict or diagnose). For example, such historical data may associatethe predicted outcomes of the application with corresponding actualoutcomes;

(c) there is a metric or distance-like evaluation function that can beapplied to the parameter space for indicating relative closeness oraccuracy of points in the parameter space, wherein the evaluationfunction provides a measurement of closeness that is related to theactual performance of the application.

Note that there are numerous applications for which the above criteriaare applicable. For instance, computer aided control of chemicalprocessing plants are likely to satisfy the above criteria. Certainrobotic applications may also satisfy this criteria. In fact, it isbelieved that a wide range of signal processing applications satisfythis criteria.

MS Status Repository Introduction

The MS status repository 1338 is a run-time storage manager for storinglocation hypotheses from previous activations of the location engine 139(as well as for storing the output “most likely” target MS locationestimate(s)) so that a target MS 140 may be tracked using target MSlocation hypotheses from previous location engine 139 activations todetermine, for example, a movement of the target MS 140 betweenevaluations of the target MS location.

Location Hypothesis Analyzer Introduction.

The location hypothesis analyzer 1332, adjusts confidence values of thelocation hypotheses, according to:

-   -   (a) heuristics and/or statistical methods related to how well        the signal characteristics for the generated target MS location        hypothesis matches with previously obtained signal        characteristics for verified MS locations.    -   (b) heuristics related to how consistent the location hypothesis        is with physical laws, and/or highly probable reasonableness        conditions relating to the location of the target MS and its        movement characteristics. For example, such heuristics may        utilize knowledge of the geographical terrain in which the MS is        estimated to be, and/or, for instance, the MS velocity,        acceleration or extrapolation of an MS position, velocity, or        acceleration.    -   (c) generation of additional location hypotheses whose MS        locations are consistent with, for example, previous estimated        locations for the target MS.

As shown in FIGS. 6 and 7, the hypothesis analyzer 1332 module receives(potentially) modified location hypotheses from the context adjuster1326 and performs additional location hypothesis processing that islikely to be common and generic in analyzing most location hypotheses.More specifically, the hypothesis analyzer 1332 may adjust either orboth of the target MS 140 estimated location and/or the confidence of alocation hypothesis. In brief, the hypothesis analyzer 1332 receivestarget MS 140 location hypotheses from the context analyzer 1336, anddepending on the time stamps of newly received location hypotheses andany previous (i.e., older) target MS location hypotheses that may stillbe currently available to the hypothesis analyzer 1332, the hypothesisanalyzer may:

(a) update some of the older hypotheses by an extrapolation module,

(b) utilize some of the old hypotheses as previous target MS estimatesfor use in tracking the target MS 140, and/or

(c) if sufficiently old, then delete the older location hypotheses.

Note that both the newly received location hypotheses and the previouslocation hypotheses that are updated (i.e., extrapolated) and stillremain in the hypothesis analyzer 1332 will be denoted as “currentlocation hypotheses” or “currently active location hypotheses”.

The modules within the location hypothesis analyzer 1332 use varioustypes of application specific knowledge likely substantially independentfrom the computations by the FOMs 1224 when providing the correspondingoriginal location hypotheses. That is, since it is an aspect of at leastone embodiment of the present disclosure that the FOMs 1224 berelatively straightforward so that they may be easily to modified aswell as added or deleted, the processing, for example, in the hypothesisanalyzer 1332 (as with the context adjuster 1326) is intended tocompensate, when necessary, for this straightforwardness by providingsubstantially generic MS location processing capabilities that canrequire a greater breadth of application understanding related towireless signal characteristics of the coverage area 120.

Accordingly, the hypothesis analyzer 1332 may apply various heuristicsthat, for example, change the confidence in a location hypothesisdepending on how well the location hypothesis (and/or a series oflocation hypotheses from e.g., the same FOM 1224): (a) conforms with thelaws of physics, (b) conforms with known characteristics of locationsignature clusters in an area of the location hypothesis MS 140estimate, and (c) conforms with highly likely heuristic constraintknowledge. In particular, as illustrated best in FIG. 7, the locationhypothesis analyzer 1332 may utilize at least one of a blackboard systemand/or an expert system for applying various application specificheuristics to the location hypotheses output by the context adjuster1326. More precisely, the location hypothesis analyzer 1332 includes, inone embodiment, a blackboard manager for managing processes and data ofa blackboard system. Additionally, note that in a second embodiment,where an expert system is utilized instead of a blackboard system, thelocation hypothesis analyzer provides an expert system inference enginefor the expert system. Note that additional detail on these aspects ofthe invention are provided hereinbelow.

Additionally, note that the hypothesis analyzer 1332 may activate one ormore extrapolation procedures to extrapolate target MS 140 locationhypotheses already processed. Thus, when one or more new locationhypotheses are supplied (by the context adjuster 1224) having asubstantially more recent timestamp, the hypothesis analyzer may invokean extrapolation module (i.e., location extrapolator 1432, FIG. 7) foradjusting any previous location hypotheses for the same target MS 140that are still being used by the location hypothesis analyzer so thatall target MS location hypotheses (for the same target MS) beingconcurrently analyzed are presumed to be for substantially the sametime. Accordingly, such a previous location hypothesis that is, forexample, 15 seconds older than a newly supplied location hypothesis(from perhaps a different FOM 1224) may have both: (a) an MS locationestimate changed (e.g., to account for a movement of the target MS), and(b) its confidence changed (e.g., to reflect a reduced confidence in theaccuracy of the location hypothesis).

It is important to note that the architecture of the present disclosureis such that the hypothesis analyzer 1332 has an extensiblearchitecture. That is, additional location hypothesis analysis modulesmay be easily integrated into the hypothesis analyzer 1332 as furtherunderstanding regarding the behavior of wireless signals within theservice area 120 becomes available. Conversely, some analysis modulesmay not be required in areas having relatively predictable signalpatterns. Thus, in such service areas, such unnecessary modules may beeasily removed or not even developed.

Most Likelihood Estimator Introduction

The most likelihood estimator 1344 is a module for determining a “mostlikely” location estimate for a target MS being located by the locationengine 139. The most likelihood estimator 1344 receives a collection ofactive or relevant location hypotheses from the hypothesis analyzer 1332and uses these location hypotheses to determine one or more most likelyestimates for the target MS 140. Still referring to the hypothesisevaluator 1228, it is important to note that not all the above mentionedmodules are required in all embodiments of the novel wireless locationsystem(s) and/or method(s) disclosed herein. In particular, for somecoverage areas 120, the hypothesis analyzer 1332 may be unnecessary.Accordingly, in such an embodiment, the enhanced location hypothesisoutput by the context adjuster 1326 are provided directly to the mostlikelihood estimator 1344.

Control and Output Gating Modules

A fourth functional group of location engine 139 modules is the controland output gating modules which includes the location center controlsubsystem 1350, and the output gateway 1356. The location controlsubsystem 1350 provides the highest level of control and monitoring ofthe data processing performed by the location center 142. In particular,this subsystem performs the following functions:

-   -   (a) controls and monitors location estimating processing for        each target MS 140. Note that this includes high level exception        or error handling functions;    -   (b) receives and routes external information as necessary. For        instance, this subsystem may receive (via, e.g., the public        telephone switching network 124 and Internet 468) such        environmental information as increased signal noise in a        particular service area due to increase traffic, a change in        weather conditions, a base station 122 (or other infrastructure        provisioning), change in operation status (e.g., operational to        inactive);    -   (c) receives and directs location processing requests from other        location centers 142 (via, e.g., the Internet 468);    -   (d) performs accounting and billing procedures;    -   (e) interacts with location center operators by, for example,        receiving operator commands and providing output indicative of        processing resources being utilized and malfunctions;    -   (f) provides access to output requirements for various        applications requesting location estimates. For example, an        Internet 468 location request from a trucking company in Los        Angeles to a location center 142 in Denver may only want to know        if a particular truck or driver is within the Denver area.        Alternatively, a local medical rescue unit is likely to request        as precise a location estimate as possible.

Note that in FIG. 6, (a)-(d) above are, at least at a high level,performed by utilizing the operator interface 1374.

Referring now to the output gateway 1356, this module routes target MS140 location estimates to the appropriate location application(s). Forinstance, upon receiving a location estimate from the most likelihoodestimator 1344, the output gateway 1356 may determine that the locationestimate is for an automobile being tracked by the police and thereforemust be provided according to a particular protocol.

System Tuning and Adaptation: The Adaptation Engine

A fifth functional group of location engine 139 modules provides theability to enhance the MS locating reliability and/or accuracy of anembodiment of a novel wireless location system and/or method disclosedherein by providing it with the capability to adapt to particularoperating configurations, operating conditions and wireless signalingenvironments without performing intensive manual analysis of theperformance of various embodiments of the location engine 139. That is,this functional group automatically enhances the performance of thelocation engine for locating MSs 140 within a particular coverage area120 using at least one wireless network infrastructure therein. Moreprecisely, this functional group allows an embodiment of a novelwireless location system and/or method disclosed herein to adapt bytuning or optimizing certain system parameters according to locationengine 139 location estimate accuracy and reliability.

There are a number location engine 139 system parameters whose valuesaffect location estimation, and it is an aspect of the presentdisclosure that the MS location processing performed should becomeincreasingly better at locating a target MS 140 not only throughbuilding an increasingly more detailed model of the signalcharacteristics of location in the coverage area 120 such as discussedabove regarding the location signature data base 1320, but also byproviding automated capabilities for the location center processing toadapt by adjusting or “tuning” the values of such location center systemparameters.

Accordingly, a system or method according to the present disclosure mayinclude a module, denoted herein as an “adaptation engine” 1382, thatperforms an optimization procedure on the location center 142 systemparameters either periodically or concurrently with the operation of thelocation center in estimating MS locations. That is, the adaptationengine 1382 directs the modifications of the system parameters so thatthe location engine 139 increases in overall accuracy in locating targetMSs 140. In one embodiment, the adaptation engine 1382 includes anembodiment of a genetic algorithm as the mechanism for modifying thesystem parameters. Genetic algorithms are basically search algorithmsbased on the mechanics of natural genetics. The genetic algorithmutilized herein is included in the form of pseudo code in APPENDIX B.Note that to apply this genetic algorithm in the context of the locationengine 139 architecture only a “coding scheme” and a “fitness function”are required as one skilled in the art will appreciate. Moreover, it isalso within the scope of the present disclosure to use modified ordifferent adaptive and/or tuning mechanisms. For further informationregarding such adaptive mechanisms, the following references areincorporated herein by reference: Goldberg, D. E. (1989). Geneticalgorithms for search, optimization, and machine learning. Reading,Mass.: Addison-Wesley Publishing Company; and Holland, J. H. (1975)Adaptation in natural and artificial systems. Ann Arbor, Mich.: TheUniversity of Michigan Press.

Implementations of First Order Models

Further descriptions of various first order models 1224 are provided inthis section.

Distance First Order Models (TOA/TDOA)

As discussed in the Location Center Architecture Overview section hereinabove, distance models determine a presumed direction and/or distancethat a target MS 140 is from one or more base stations 122. In someembodiments of distance models, the target MS location estimate(s)generated are obtained using radio signal analysis techniques that arequite general and therefore are not capable of taking into account thepeculiarities of the topography of a particular radio coverage area. Forexample, substantially all radio signal analysis techniques usingconventional procedures (or formulas) are based on “signalcharacteristic measurements” such as:

(a) signal timing measurements (e.g., TOA and TDOA),

(b) signal strength measurements, and/or

(c) signal angle of arrival measurements.

Furthermore, such signal analysis techniques are likely predicated oncertain very general assumptions that can not fully account for signalattenuation and multipath due to a particular radio coverage areatopography.

Taking a CDMA or TDMA base station network as an example, each basestation (BS) 122 is required to emit a constant signal-strength pilotchannel pseudo-noise (PN) sequence on the forward link channelidentified uniquely in the network by a pilot sequence offset andfrequency assignment. It is possible to use the pilot channels of theactive, candidate, neighboring and remaining sets, maintained in thetarget MS, for obtaining signal characteristic measurements (e.g., TOAand/or TDOA measurements) between the target MS 140 and the basestations in one or more of these sets.

Based on such signal characteristic measurements and the speed of signalpropagation, signal characteristic ranges or range differences relatedto the location of the target MS 140 can be calculated. Using TOA and/orTDOA ranges as exemplary, these ranges can then be input to either theradius-radius multilateration or the time difference multilaterationalgorithms along with the known positions of the corresponding basestations 122 to thereby obtain one or more location estimates of thetarget MS 140. For example, if there are, four base stations 122 in theactive set, the target MS 140 may cooperate with each of the basestations in this set to provide signal arrival time measurements.Accordingly, each of the resulting four sets of three of these basestations 122 may be used to provide an estimate of the target MS 140 asone skilled in the art will understand. Thus, potentially (assuming themeasurements for each set of three base stations yields a feasiblelocation solution) there are four estimates for the location of thetarget MS 140. Further, since such measurements and BS 122 positions canbe sent either to the network or the target MS 140, location can bedetermined in either entity.

Since many of the signal measurements utilized by embodiments ofdistance models are subject to signal attenuation and multipath due to aparticular area topography. Many of the sets of base stations from whichtarget MS location estimates are desired may result in either nolocation estimate, or an inaccurate location estimate.

Accordingly, some embodiments of distance FOMs may attempt to mitigatesuch ambiguity or inaccuracies by, e.g., identifying discrepancies (orconsistencies) between arrival time measurements and other measurements(e.g., signal strength), these discrepancies (or consistencies) may beused to filter out at least those signal measurements and/or generatedlocation estimates that appear less accurate. In particular, suchidentifying by filtering can be performed by, for example, an expertsystem residing in the distance FOM.

A second approach for mitigating such ambiguity or conflicting MSlocation estimates is particularly novel in that each of the target MSlocation estimates is used to generate a location hypothesis regardlessof its apparent accuracy. Accordingly, these location hypotheses areinput to an alternative embodiment of the context adjuster 1326 that issubstantially (but not identical to) the context adjuster as describedin detail in APPENDIX D so that each location hypothesis may be adjustedto enhance its accuracy. In contradistinction to the embodiment of thecontext adjuster 1326 of APPENDIX D, where each location hypothesis isadjusted according to past performance of its generating FOM 1224 in anarea of the initial location estimate of the location hypothesis (thearea, e.g., determined as a function of distance from this initiallocation estimate), this alternative embodiment adjusts each of thelocation hypotheses generated by a distance first order model accordingto a past performance of the model as applied to signal characteristicmeasurements from the same set of base stations 122 as were used ingenerating the location hypothesis. That is, instead of only using onlyan identification of the distance model (i.e., its FOM_ID) to, forexample, retrieve archived location estimates generated by the model inan area of the location hypothesis' estimate (when determining themodel's past performance), the retrieval retrieves only the archivedlocation estimates that are, in addition, derived from the signalcharacteristics measurement obtained from the same collection of basestations 122 as was used in generating the location hypothesis. Thus,the adjustment performed by this embodiment of the context adjuster 1326adjusts according to the past performance of the distance model and thecollection of base stations 122 used.

Coverage Area First Order Model

Radio coverage area of individual base stations 122 may be used togenerate location estimates of the target MS 140. Although a first ordermodel 1224 based on this notion may be less accurate than othertechniques, if a reasonably accurate RF coverage area is known for each(or most) of the base stations 122, then such a FOM (denoted hereinafteras a “coverage area first order model” or simply “coverage area model”)may be very reliable. To determine approximate maximum radio frequency(RF) location coverage areas, with respect to BSs 122, antennas and/orsector coverage areas, for a given class (or classes) of (e.g., CDMA orTDMA) mobile station(s) 140, location coverage should be based on anMS's ability to adequately detect the pilot channel, as opposed toadequate signal quality for purposes of carrying user-acceptable trafficin the voice channel. Note that more energy is necessary for trafficchannel activity (typically on the order of at least −94 to −104 dBmreceived signal strength) to support voice, than energy needed to simplydetect a pilot channel's presence for location purposes (typically amaximum weakest signal strength range of between −104 to −110 dBm), thusthe “Location Coverage Area” will generally be a larger area than thatof a typical “Voice Coverage Area”, although industry studies have foundsome occurrences of “no-coverage” areas within a larger covered area. Anexample of a coverage area including both a “dead zone”, i.e., area ofno coverage, and a “notch” (of also no coverage) is shown in FIG. 15.

The approximate maximum RF coverage area for a given sector of (moregenerally angular range about) a base station 122 may be represented asa set of points representing a polygonal area (potentially with, e.g.,holes therein to account for dead zones and/or notches). Note that ifsuch polygonal RF coverage area representations can be reliablydetermined and maintained over time (for one or more BS signal powerlevel settings), then such representations can be used in providing aset theoretic or Venn diagram approach to estimating the location of atarget MS 140. Coverage area first order models utilize such anapproach.

In one embodiment, a coverage area model utilizes both the detection andnon-detection of base stations 122 by the target MS 140 (conversely, ofthe MS by one or more base stations 122) to define an area where thetarget MS 140 may likely be. A relatively straightforward application ofthis technique is to:

-   -   (a) find all areas of intersection for base station RF coverage        area representations, wherein: (i) the corresponding base        stations are on-line for communicating with MSs 140; (ii) the RF        coverage area representations are deemed reliable for the power        levels of the on-line base stations; (iii) the on-line base        stations having reliable coverage area representations can be        detected by the target MS; and (iv) each intersection must        include a predetermined number of the reliable RF coverage area        representations (e.g., 2 or 3); and    -   (b) obtain new location estimates by subtracting from each of        the areas of intersection any of the reliable RF coverage area        representations for base stations 122 that can not be detected        by the target MS.

Accordingly, the new areas may be used to generate location hypotheses.

Location Base Station First Order Model

In the location base station (LBS) model (FOM 1224), a database isaccessed which contains electrical, radio propagation and coverage areacharacteristics of each of the location base stations in the radiocoverage area. The LBS model is an active model, in that it can probe orexcite one or more particular LBSs 152 in an area for which the targetMS 140 to be located is suspected to be placed. Accordingly, the LBSmodel may receive as input a most likely target MS 140 location estimatepreviously output by the location engine 139, and use this locationestimate to determine which (if any) LBSs 152 to activate and/ordeactivate for enhancing a subsequent location estimate of the targetMS. Moreover, the feedback from the activated LBSs 152 may be providedto other FOMs 1224, as appropriate, as well as to the LBS model.However, it is an important aspect of the LBS model that when itreceives such feedback, it may output location hypotheses havingrelatively small target MS 140 location area estimates about the activeLBSs 152 and each such location hypothesis also has a high confidencevalue indicative of the target MS 140 positively being in thecorresponding location area estimate (e.g., a confidence value of 0.9 to+1), or having a high confidence value indicative of the target MS 140not being in the corresponding location area estimate (i.e., aconfidence value of −0.9 to −1). Note that in some embodiments of theLBS model, these embodiments may have functionality similar to that ofthe coverage area first order model described above. Further note thatfor LBSs within a neighborhood of the target MS wherein there is areasonable chance that with movement of the target MS may be detected bythese LBSs, such LBSs may be requested to periodically activate. (Note,that it is not assumed that such LBSs have an on-line external powersource; e.g., some may be solar powered). Moreover, in the case where anLBS 152 includes sufficient electronics to carry voice communicationwith the target MS 140 and is the primary BS for the target MS (oralternatively, in the active or candidate set), then the LBS model willnot deactivate this particular LBS during its procedure of activatingand deactivating various LBSs 152.

Stochastic First Order Model

The stochastic first order models may use statistical predictiontechniques such as principle decomposition, partial least squares, orother regression techniques for predicting, for example, expectedminimum and maximum distances of the target MS from one or more basestations 122, e.g., Bollenger Bands. Additionally, some embodiments mayuse Markov processes and Random Walks (predicted incremental MSmovement) for determining an expected area within which the target MS140 is likely to be. That is, such a process measures the incrementaltime differences of each pilot as the MS moves for predicting a size ofa location area estimate using past MS estimates such as the verifiedlocation signatures in the location signature data base 1320.

Pattern Recognition and Adaptive First Order Models

It is a particularly important aspect of the present disclosure toprovide:

-   -   (a) one or more FOMs 1224 that generate target MS 140 location        estimates by using pattern recognition or associativity        techniques, and/or    -   (b) one or more FOMs 1224 that are adaptive or trainable so that        such FOMs may generate increasingly more accurate target MS        location estimates from additional training.        Statistically Based Pattern Recognition First Order Models

Regarding FOMs 1224 using pattern recognition or associativitytechniques, there are many such techniques available. For example, thereare statistically based systems such as “CART” (an acronym forClassification and Regression Trees) by ANGOSS Software InternationalLimited of Toronto, Canada that may be used for automatically detectingor recognizing patterns in data that were unprovided (and likelypreviously unknown). Accordingly, by imposing a relatively fine mesh orgrid of cells on the radio coverage area, wherein each cell is entirelywithin a particular area type categorization such as the transmissionarea types (discussed in the section, “Coverage Area: Area Types AndTheir Determination” above), the verified location signature clusterswithin the cells of each area type may be analyzed for signalcharacteristic patterns. If such patterns are found, then they can beused to identify at least a likely area type in which a target MS islikely to be located. That is, one or more location hypotheses may begenerated having target MS 140 location estimates that cover an areahaving the likely area type wherein the target MS 140 is located.Further note that such statistically based pattern recognition systemsas “CART” include software code generators for generating expert systemsoftware embodiments for recognizing the patterns detected within atraining set (e.g., the verified location signature clusters).

Accordingly, although an embodiment of a FOM as described here may notbe exceedingly accurate, it may be very reliable. Thus, since an aspectof the present disclosure is to use a plurality MS location techniquesfor generating location estimates and to analyze the generated estimates(likely after being adjusted) to detect patterns of convergence orclustering among the estimates, even large MS location area estimatesare useful. For example, it can be the case that four different andrelatively large MS location estimates, each having very highreliability, have an area of intersection that is acceptably precise andinherits the very high reliability from each of the large MS locationestimates from which the intersection area was derived.

A similar statistically based FOM 1224 to the one above may be providedwherein the radio coverage area is decomposed substantially as above,but in addition to using the signal characteristics for detecting usefulsignal patterns, the specific identifications of the base station 122providing the signal characteristics may also be used. Thus, assumingthere is a sufficient density of verified location signature clusters insome of the mesh cells so that the statistical pattern recognizer candetect patterns in the signal characteristic measurements, an expertsystem may be generated that outputs a target MS 140 location estimatethat may provide both a reliable and accurate location estimate of atarget MS 140.

Adaptive/Trainable First Order Models

The term adaptive is used to describe a data processing component thatcan modify its data processing behavior in response to certain inputsthat are used to change how subsequent inputs are processed by thecomponent. Accordingly, a data processing component may be “explicitlyadaptive” by modifying its behavior according to the input of explicitinstructions or control data that is input for changing the component'ssubsequent behavior in ways that are predictable and expected. That is,the input encodes explicit instructions that are known by a user of thecomponent. Alternatively, a data processing component may be “implicitlyadaptive” in that its behavior is modified by other than instructions orcontrol data whose meaning is known by a user of the component. Forexample, such implicitly adaptive data processors may learn by trainingon examples, by substantially unguided exploration of a solution space,or other data driven adaptive strategies such as statistically generateddecision trees. Accordingly, it is an aspect of the present disclosureto utilize not only explicitly adaptive MS location estimators withinFOMs 1224, but also implicitly adaptive MS location estimators. Inparticular, artificial neural networks (also denoted neural nets andANNs herein) are used in some embodiments as implicitly adaptive MSlocation estimators within FOMs. Thus, in the sections below, neural netarchitectures and their application to locating an MS is described.

Artificial Neural Networks for MS Location

Artificial neural networks may be particularly useful in developing oneor more first order models 1224 for locating an MS 140, since, forexample, ANNs can be trained for classifying and/or associativelypattern matching of various RF signal measurements such as the locationsignatures. That is, by training one or more artificial neural netsusing RF signal measurements from verified locations so that RF signaltransmissions characteristics indicative of particular locations areassociated with their corresponding locations, such trained artificialneural nets can be used to provide additional target MS 140 locationhypotheses. Moreover, it is an aspect of the present disclosure that thetraining of such artificial neural net based FOMs (ANN FOMs) is providedwithout manual intervention as will be discussed hereinbelow.

Artificial Neural Networks that Converge on Near Optimal Solutions

It is as an aspect of the present disclosure to use an adaptive neuralnetwork architecture which has the ability to explore the parameter ormatrix weight space corresponding to a ANN for determining newconfigurations of weights that reduce an objective or error functionindicating the error in the output of the ANN over some aggregate set ofinput data ensembles. Accordingly, in one embodiment, a geneticalgorithm is used to provide such an adaptation capability. However, itis also within the scope of the present disclosure to use other adaptivetechniques such as, for example, simulated annealing, cascadecorrelation with multistarts, gradient descent with multistarts, andtruncated Newton's method with multistarts, as one skilled in the art ofneural network computing will understand.

Artificial Neural Networks as MS Location Estimators for First OrderModels

Although there have been substantial advances in artificial neural netcomputing in both hardware and software, it can be difficult to choose aparticular ANN architecture and appropriate training data for yieldinghigh quality results. In choosing a ANN architecture at least thefollowing three criteria are chosen (either implicitly or explicitly):

-   -   (a) a learning paradigm: i.e., does the ANN require supervised        training (i.e., being provided with indications of correct and        incorrect performance), unsupervised training, or a hybrid of        both (sometimes referred to as reinforcement);    -   (b) a collection of learning rules for indicating how to update        the ANN;    -   (c) a learning algorithm for using the learning rules for        adjusting the ANN weights.        Furthermore, there are other implementation issues such as:    -   (d) how many layers a artificial neural net should have to        effectively capture the patterns embedded within the training        data. For example, the benefits of using small ANN are many.        less costly to implement, faster, and tend to generalize better        because they avoid overfitting weights to training patterns.        That is, in general, more unknown parameters (weights) induce        more local and global minima in the error surface or space.        However, the error surface of smaller nets can be very rugged        and have few good solutions, making it difficult for a local        minimization algorithm to find a good solution from a random        starting point as one skilled in the art will understand;    -   (e) how many units or neurons to provide per layer;    -   (f) how large should the training set be presented to provide        effective generalization to non-training data    -   (g) what type of transfer functions should be used.

However, the architecture of the present disclosure allows substantialflexibility in the implementation of ANN for FOMs 1224. In particular,there is no need to choose only one artificial neural net architectureand/or implementation in that a plurality of ANNs may be accommodated bythe architecture of the location engine 139. Furthermore, it isimportant to keep in mind that it may not be necessary to train a ANNfor a FOM as rigorously as is done in typical ANN applications since theaccuracy and reliability in estimating the location of a target MS 140may be provided by synergistically utilizing a plurality of different MSlocation estimators, each of which may be undesirable in terms ofaccuracy and/or reliability in some areas, but when their estimates aresynergistically used as in the location engine 139, accurate andreliable location estimates can be attained. Accordingly, one embodimentof the present disclosure may have a plurality of moderately welltrained ANNs having different neural net architectures such as:multilayer perceptrons, adaptive resonance theory models, and radialbasis function networks.

Additionally, many of the above mentioned ANN architecture andimplementation decisions can be addressed substantially automatically byvarious commercial artificial neural net development systems such as:“NEUROGENETIC OPTIMIZER” by BioComp Systems Inc., 4018 148th Ave. NE,Redmond, Wash. 98052, USA wherein genetic algorithms are used tooptimize and configure ANNs, and artificial neural network hardware andsoftware products by Accurate Automation Corporation of Chattanooga,Tenn., such as “ACCURATE AUTOMATION NEURAL NETWORK TOOLS.

Artificial Neural Network Input and Output

It is worthwhile to discuss the data representations for the inputs andoutputs of a ANN used for generating MS location estimates. RegardingANN input representations, recall that the signal processing subsystem1220 may provide various RF signal measurements as input to an ANN (suchas the RF signal measurements derived from verified location signaturesin the location signature data base 1320). For example, a representationof a histogram of the frequency of occurrence of CDMA fingers in a timedelay versus signal strength 2-dimensional domain may be provided asinput to such an ANN. In particular, a 2-dimensional grid of signalstrength versus time delay bins may be provided so that received signalmeasurements are slotted into an appropriate bin of the grid. In oneembodiment, such a grid is a six by six array of bins such asillustrated in the left portion of FIG. 14. That is, each of the signalstrength and time delay axes are partitioned into six ranges so thatboth the signal strength and the time delay of RF signal measurementscan be slotted into an appropriate range, thus determining the bin.

Note that RF signal measurement data (i.e., location signatures) slottedinto a grid of bins provides a convenient mechanism for classifying RFmeasurements received over time so that when each new RF measurementdata is assigned to its bin, a counter for the bin can be incremented.Thus in one embodiment, the RF measurements for each bin can berepresented pictorially as a histogram. In any case, once the RFmeasurements have been slotted into a grid, various filters may beapplied for filtering outliers and noise prior to inputting bin valuesto an ANN. Further, various amounts of data from such a grid may beprovided to an ANN. In one embodiment, the tally from each bin isprovided to an ANN. Thus, as many as 108 values could be input to theANN (two values defining each bin, and a tally for the bin). However,other representations are also possible. For instance, by ordering thebin tallies linearly, only 36 need be provided as ANN input.Alternatively, only representations of bins having the highest talliesmay be provided as ANN input. Thus, for example, if the highest 10 binsand their tallies were provided as ANN input, then only 20 inputs needbe provided (i.e., 10 input pairs, each having a single bin identifierand a corresponding tally).

In addition, note that the signal processing subsystem 1220 may alsoobtain the identifications of other base stations 122 (152) for whichtheir pilot channels can be detected by the target MS 140 (i.e., theforward path), or for which the base stations can detect a signal fromthe target MS (i.e., the reverse path). Thus, in order to effectivelyutilize substantially all pertinent location RF signal measurements(i.e., from location signature data derived from communications betweenthe target MS 140 and the base station infrastructure), a technique isprovided wherein a plurality of ANNs may be activated using variousportions of an ensemble of location signature data obtained. However,before describing this technique, it is worthwhile to note that a naivestrategy of providing input to a single ANN for locating target MSsthroughout an area having a large number of base stations (e.g., 300) islikely to be undesirable. That is, given that each base station (antennasector) nearby the target MS is potentially able to provide the ANN withlocation signature data, the ANN would have to be extremely large andtherefore may require inordinate training and retraining. For example,since there may be approximately 30 to 60 ANN inputs per locationsignature, an ANN for an area having even twenty base stations 122 canrequire at least 600 input neurons, and potentially as many as 1,420(i.e., 20 base stations with 70 inputs per base station and one inputfor every one of possibly 20 additional surrounding base stations in theradio coverage area 120 that might be able to detect, or be detected by,a target MS 140 in the area corresponding to the ANN).

ANN Input

Accordingly, the technique described herein limits the number of inputneurons in each ANN constructed and generates a larger number of thesesmaller ANNs. That is, each ANN is trained on location signature data(or, more precisely, portions of location signature clusters) in an areaA_(ANN) (hereinafter also denoted the “net area”), wherein each inputneuron receives a unique input from one of:

-   (A1) location signature data (e.g., signal strength/time delay bin    tallies) corresponding to transmissions between an MS 140 and a    relatively small number of base stations 122 in the area A_(ANN) For    instance, location signature data obtained from, for example, a    collection B of four base stations 122 (or antenna sectors) in the    area A_(ANN). Note, each location signature data cluster includes    fields describing the wireless communication devices used; e.g., (i)    the make and model of the target MS; (ii) the current and maximum    transmission power; (iii) the MS battery power (instantaneous or    current); (iv) the base station (sector) current power level; (v)    the base station make and model and revision level; (vi) the air    interface type and revision level (of, e.g., CDMA, TDMA or AMPS).-   (A2) a discrete input corresponding to each base station 122 (or    antenna sector 130) in a larger area containing A_(ANN), wherein    each such input here indicates whether the corresponding base    station (sector):    -   (i) is on-line (i.e., capable of wireless communication with        MSs) and at least its pilot channel signal is detected by the        target MS 140, but the base station (sector) does not detect the        target MS;    -   (ii) is on-line and the base station (sector) detects a wireless        transmission from the target MS, but the target MS does not        detect the base station (sector) pilot channel signal;    -   (iii) is on-line and the base station (sector) detects the        target MS and the base station (sector) is detected by the        target MS;    -   (iv) is on-line and the base station (sector) does not detect        the target MS, the base station is not detected by the target        MS; or    -   (v) is off-line (i.e., incapable of wireless communication with        one or more MSs).        Note that (i)-(v) are hereinafter referred to as the “detection        states.”

Thus, by generating an ANN for each of a plurality of net areas(potentially overlapping), a local environmental change in the wirelesssignal characteristics of one net area is unlikely to affect more than asmall number of adjacent or overlapping net areas. Accordingly, suchlocal environmental changes can be reflected in that only the ANNshaving net areas affected by the local change need to be retrained.Additionally, note that in cases where RF measurements from a target MS140 are received across multiple net areas, multiple ANNs may beactivated, thus providing multiple MS location estimates. Further,multiple ANNs may be activated when a location signature cluster isreceived for a target MS 140 and location signature cluster includeslocation signature data corresponding to wireless transmissions betweenthe MS and, e.g., more base stations (antenna sectors) than needed forthe collection B described in the previous section. That is, if eachcollection B identifies four base stations 122 (antenna sectors), and areceived location signature cluster includes location signature datacorresponding to five base stations (antenna sectors), then there may beup to five ANNs activated to each generate a location estimate.

Moreover, for each of the smaller ANNs, it is likely that the number ofinput neurons is on the order of 330; (i.e., 70 inputs per each of fourlocation signatures (i.e., 35 inputs for the forward wirelesscommunications and 35 for the reverse wireless communications), plus 40additional discrete inputs for an appropriate area surrounding A_(ANN),plus 10 inputs related to: the type of MS, power levels, etc. However,it is important to note that the number of base stations (or antennasectors 130) having corresponding location signature data to be providedto such an ANN may vary. Thus, in some subareas of the coverage area120, location signature data from five or more base stations (antennasectors) may be used, whereas in other subareas three (or less) may beused.

Regarding the output from ANNs used in generating MS location estimates,there are also numerous options. In one embodiment, two valuescorresponding to the latitude and longitude of the target MS areestimated. Alternatively, by applying a mesh to the coverage area 120,such ANN output may be in the form of a row value and a column value ofa particular mesh cell (and its corresponding area) where the target MSis estimated to be. Note that the cell sizes of the mesh need not be ofa particular shape nor of uniform size. However, simple non-oblongshapes are desirable. Moreover, such cells should be sized so that eachcell has an area approximately the size of the maximum degree oflocation precision desired. Thus, assuming square mesh cells, 250 to 350feet per cell side in an urban/suburban area, and 500 to 700 feet percell side in a rural area may be desirable.

Artificial Neural Network Training

The following are steps provided in one embodiment for training alocation estimating ANN.

-   -   (a) Determine a collection, C, of clusters of RF signal        measurements (i.e., location signatures) such that each cluster        is for RF transmissions between an MS 140 and a common set, B,        of base stations 122 (or antenna sectors 130) such measurements        are as described in (A1) above. In one embodiment, the        collection C is determined by interrogating the location        signature data base 1320 for verified location signature        clusters stored therein having such a common set B of base        stations (antenna sectors). Alternatively in another embodiment,        note that the collection C may be determined from (i) the        existing engineering and planning data from service providers        who are planning wireless cell sites, or (ii) service provider        test data obtained using mobile test sets, access probes or        other RF field measuring devices. Note that such a collection B        of base stations (antenna sectors) should only be created when        the set C of verified location signature clusters is of a        sufficient size so that it is expected that the ANN can be        effectively trained.    -   (b) Determine a collection of base stations (or antenna sectors        130), B′, from the common set B, wherein B′ is small (e.g., four        or five).    -   (c) Determine the area, A_(ANN), to be associated with        collection B′ of base stations (antenna sectors). In one        embodiment, this area is selected by determining an area        containing the set L of locations of all verified location        signature clusters determined in step (a) having location        signature data from each of the base stations (antenna sectors)        in the collection B′. More precisely, the area, A_(ANN), may be        determined by providing a covering of the locations of L, such        as, e.g., by cells of a mesh of appropriately fine mesh size so        that each cell is of a size not substantially larger than the        maximum MS location accuracy desired.    -   (d) Determine an additional collection, b, of base stations that        have been previously detected (and/or are likely to be detected)        by at least one MS in the area A_(ANN).    -   (e) Train the ANN on input data related to: (i) signal        characteristic measurements of signal transmissions between MSs        140 at verified locations in A_(ANN), and the base stations        (antenna sectors) in the collection B′, and (ii) discrete inputs        of detection states from the base stations represented in the        collection b. For example, train the ANN on input including: (i)        data from verified location signatures from each of the base        stations (antenna sectors) in the collection B′, wherein each        location signature is part of a cluster in the collection        C; (ii) a collection of discrete values corresponding to other        base stations (antenna sectors) in the area b containing the        area, A_(ANN).

Regarding (d) immediately above, it is important to note that it isbelieved that less accuracy is required in training a ANN used forgenerating a location hypothesis (in a FOM 1224) for an embodiment of anovel wireless location system and/or method disclosed herein than inmost applications of ANNs (or other trainable/adaptive components)since, in most circumstances, when signal measurements are provided forlocating a target MS 140, the location engine 139 will activate aplurality location hypothesis generating modules (corresponding to oneor more FOMs 1224) for substantially simultaneously generating aplurality of different location estimates (i.e., hypotheses). Thus,instead of training each ANN so that it is expected to be, e.g., 92% orhigher in accuracy, it is believed that synergies with MS locationestimates from other location hypothesis generating components willeffectively compensate for any reduced accuracy in such a ANN (or anyother location hypothesis generating component). Accordingly, it isbelieved that training time for such ANNs may be reduced withoutsubstantially impacting the MS locating performance of the locationengine 139.

Finding Near-Optimal Location Estimating Artificial Neural Networks

In one traditional artificial neural network training process, arelatively tedious set of trial and error steps may be performed forconfiguring an ANN so that training produces effective learning. Inparticular, an ANN may require configuring parameters related to, forexample, input data scaling, test/training set classification, detectingand removing unnecessary input variable selection. However, the presentdisclosure reduces this tedium. That is, the present disclosure providesmechanisms such as genetic algorithms or other mechanisms for avoidingnon-optimal but locally appealing (i.e., local minimum) solutions, andlocating near-optimal solutions instead. In particular, such mechanismmay be used to adjust the matrix of weights for the ANNs so that verygood, near optimal ANN configurations may be found efficiently.Furthermore, since the signal processing system 1220 uses various typesof signal processing filters for filtering the RF measurements receivedfrom transmissions between an MS 140 and one or more base stations(antenna sectors 130), such mechanisms for finding near-optimalsolutions may be applied to selecting appropriate filters as well.Accordingly, in one embodiment of the present disclosure, such filtersare paired with particular ANNs so that the location signature datasupplied to each ANN is filtered according to a corresponding “filterdescription” for the ANN, wherein the filter description specifies thefilters to be used on location signature data prior to inputting thisdata to the ANN. In particular, the filter description can define apipeline of filters having a sequence of filters wherein for each twoconsecutive filters, f₁ and f₂ (f₁ preceding f₂), in a filterdescription, the output of f₁ flows as input to f₂. Accordingly, byencoding such a filter description together with its corresponding ANNso that the encoding can be provided to a near optimal solution findingmechanism such as a genetic algorithm, it is believed that enhanced ANNlocating performance can be obtained. That is, the combined geneticcodes of the filter description and the ANN are manipulated by thegenetic algorithm in a search for a satisfactory solution (i.e.,location error estimates within a desired range). This process andsystem provides a mechanism for optimizing not only the artificialneural network architecture, but also identifying a near optimal matchbetween the ANN and one or more signal processing filters. Accordingly,the following filters may be used in a filter pipeline of a filterdescription: Sobel, median, mean, histogram normalization, inputcropping, neighbor, Gaussian, Weiner filters.

One embodiment for implementing the genetic evolving of filterdescription and ANN pairs is provided by the following steps that mayautomatically performed without substantial manual effort:

-   -   1) Create an initial population of concatenated genotypes, or        genetic representations for each pair of an artificial neural        networks and corresponding filter description pair. Also,        provide seed parameters which guide the scope and        characterization of the artificial neural network architectures,        filter selection and parameters, genetic parameters and system        control parameters.    -   2) Prepare the input or training data, including, for example,        any scaling and normalization of the data.    -   3) Build phenotypes, or artificial neural network/filter        description combinations based on the genotypes.    -   4) Train and test the artificial neural network/filter        description phenotype combinations to determine fitness; e.g.,        determine an aggregate location error measurement for each        network/filter description phenotype.    -   5) Compare the fitnesses and/or errors, and retain the best        network/filter description phenotypes.    -   6) Select the best networks/filter descriptions in the phenotype        population (i.e., the combinations with small errors).    -   7) Repopulate the population of genotypes for the artificial        neural networks and the filter descriptions back to a        predetermined size using the selected phenotypes.    -   8) Combine the artificial neural network genotypes and filter        description genotypes thereby obtaining artificial neural        network/filter combination genotypes.    -   9) Mate the combination genotypes by exchanging genes or        characteristics/features of the network/filter combinations.    -   10) If system parameter stopping criteria is not satisfied,        return to step 3.

Note that artificial neural network genotypes may be formed by selectingvarious types of artificial neural network architectures suited tofunction approximation, such as fast back propagation, as well ascharacterizing several varieties of candidate transfer/activationfunctions, such as Tan h, logistic, linear, sigmoid and radial basis.Furthermore, ANNs having complex inputs may be selected (as determinedby a filter type in the signal processing subsystem 1220) for thegenotypes.

Examples of genetic parameters include: (a) maximum population size(typical default: 300), (b) generation limit (typical default: 50), (c)selection criteria, such as a certain percentage to survive (typicaldefault: 0.5) or roulette wheel, (d) population refilling, such asrandom or cloning (default), (e) mating criteria, such as tail swapping(default) or two cut swapping, (f) rate for a choice of mutationcriterion, such as random exchange (default: 0.25) or section reversal,(g) population size of the concatenated artificial neural network/filtercombinations, (h) use of statistical seeding on the initial populationto bias the random initialization toward stronger first order relatingvariables, and (i) neural node influence factors, e.g., input nodes andhidden nodes. Such parameters can be used as weighting factors thatinfluences the degree the system optimizes for accuracy versus networkcompactness. For example, an input node factor greater than 0 provides ameans to reward artificial neural networks constructed that use fewerinput variables (nodes). A reasonable default value is 0.1 for bothinput and hidden node factors.

Examples of neural net/filter description system control parametersinclude: (a) accuracy of modeling parameters, such as relative accuracy,R-squared, mean squared error, root mean squared error or averageabsolute error (default), and (b) stopping criteria parameters, such asgenerations run, elapsed time, best accuracy found and populationconvergence.

Locating a Mobile Station Using Artificial Neural Networks

When using an artificial neural network for estimating a location of anMS 140, it is important that the artificial neural network be providedwith as much accurate RF signal measurement data regarding signaltransmissions between the target MS 140 and the base stationinfrastructure as possible. In particular, assuming ANN inputs asdescribed hereinabove, it is desirable to obtain the detection states ofas many surrounding base stations as possible. Thus, whenever thelocation engine 139 is requested to locate a target MS 140 (and inparticular in an emergency context such as an emergency 911 call), thelocation center 140 automatically transmits a request to the wirelessinfrastructure to which the target MS is assigned for instructing the MSto raise its transmission power to full power for a short period of time(e.g., 100 milliseconds in a base station infrastructure configurationan optimized for such requests to 2 seconds in a non-optimizedconfiguration). Note that the request for a change in the transmissionpower level of the target MS has a further advantage for locationrequests such as emergency 911 that are initiated from the MS itself inthat a first ensemble of RF signal measurements can be provided to thelocation engine 139 at the initial 911 calling power level and then asecond ensemble of RF signal measurements can be provided at a secondhigher transmission power level. Thus, in one embodiment of the presentdisclosure, an artificial neural network can be trained not only on thelocation signature cluster derived from either the initial wireless 911transmissions or the full power transmissions, but also on thedifferences between these two transmissions. In particular, thedifference in the detection states of the discrete ANN inputs betweenthe two transmission power levels may provide useful additionalinformation for more accurately estimating a location of a target MS.

It is important to note that when gathering RF signal measurements froma wireless base station network for locating MSs, the network should notbe overburdened with location related traffic. Accordingly, note thatnetwork location data requests for data particularly useful for ANNbased FOMs is generally confined to the requests to the base stations inthe immediate area of a target MS 140 whose location is desired. Forinstance, both collections of base stations B′ and b discussed in thecontext of training an ANN are also the same collections of basestations from which MS location data would be requested. Thus, thewireless network MS location data requests are data driven in that thebase stations to queried for location data (i.e., the collections B′ andb) are determined by previous RF signal measurement characteristicsrecorded. Accordingly, the selection of the collections B′ and b areadaptable to changes in the wireless environmental characteristics ofthe coverage area 120.

Location Signature Data Base

Before proceeding with a description of other levels of the presentdisclosure as described in (24.1) through (24.3) above, in this sectionfurther detail is provided regarding the location signature data base1320. Note that a brief description of the location signature data basewas provided above indicating that this data base stores MS locationdata from verified and/or known locations (optionally with additionalknown environmental characteristic values) for use in enhancing currenttarget MS location hypotheses and for comparing archived location datawith location signal data obtained from a current target MS. However,the data base management system functionality incorporated into thelocation signature data base 1320 is an important aspect of the presentdisclosure, and is therefore described in this section. In particular,the data base management functionality described herein addresses anumber of difficulties encountered in maintaining a large archive ofsignal processing data such as MS signal location data. Some of thesedifficulties can be described as follows:

-   -   (a) in many signal processing contexts, in order to effectively        utilize archived signal processing data for enhancing the        performance of a related signal processing application, there        must be an large amount of signal related data in the archive,        and this data must be adequately maintained so that as archived        signal data becomes less useful to the corresponding signal        processing application (i.e., the data becomes “inapplicable”)        its impact on the application should be correspondingly reduced.        Moreover, as archive data becomes substantially inapplicable, it        should be filtered from the archive altogether. However, the        size of the data in the archive makes it prohibitive for such a        process to be performed manually, and there may be no simple or        straightforward techniques for automating such impact reduction        or filtering processes for inapplicable signal data;    -   (b) it is sometimes difficult to determine the archived data to        use in comparing with newly obtained signal processing        application data; and    -   (c) it is sometimes difficult to determine a useful technique        for comparing archived data with newly obtained signal        processing application data.

It is an aspect of the present disclosure that the data base managementfunctionality of the location signature data base 1320 addresses each ofthe difficulties mentioned immediately above. For example, regarding(a), the location signature data base is “self cleaning” in that byassociating a confidence value with each loc sig in the data base and byreducing or increasing the confidences of archived verified loc sigsaccording to how well their signal characteristic data compares withnewly received verified location signature data, the location signaturedata base 1320 maintains a consistency with newly verified loc sigs.

The following data base management functional descriptions describe someof the more noteworthy functions of the location signature data base1320. Note that there are various ways that these functions may beembodied. So as to not overburden the reader here, the details for oneembodiment is provided in APPENDIX C. FIGS. 16 a through 16 c present atable providing a brief description of the attributes of the locationsignature data type stored in the location signature data base 1320.

Location Signature Program Descriptions

The following program updates the random loc sigs in the locationsignature data base 1320. In one embodiment, this program is invokedprimarily by the Signal Processing Subsystem.

Update Location Signature Database Program

Update_Loc_Sig_DB(new_loc_obj, selection_criteria, loc_sig_pop)

-   -   /* This program updates loc sigs in the location signature data        base 1320. That is, this program updates, for example, at least        the location information for verified random loc sigs residing        in this data base. The general strategy here is to use        information (i.e., “new_loc_obj”) received from a newly verified        location (that may not yet be entered into the location        signature data base) to assist in determining if the previously        stored random verified loc sigs are still reasonably valid to        use for:        -   (29.1) estimating a location for a given collection (i.e.,            “bag”) of wireless (e.g., CDMA) location related signal            characteristics received from an MS,        -   (29.2) training (for example) adaptive location estimators            (and location hypothesizing models), and        -   (29.3) comparing with wireless signal characteristics used            in generating an MS location hypothesis by one of the MS            location hypothesizing models (denoted First Order Models,            or, FOMs).    -   More precisely, since it is assumed that it is more likely that        the newest location information obtained is more indicative of        the wireless (CDMA) signal characteristics within some area        surrounding a newly verified location than the verified loc sigs        (location signatures) previously entered into the Location        Signature data base, such verified loc sigs are compared for        signal characteristic consistency with the newly verified        location information (object) input here for determining whether        some of these “older” data base verified loc sigs still        appropriately characterize their associated location.        -   In particular, comparisons are iteratively made here between            each (target) loc sig “near” “new_loc_obj” and a population            of loc sigs in the location signature data base 1320 (such            population typically including the loc sig for “new_loc_obj)            for:        -   (29.4) adjusting a confidence factor of the target loc sig.            Note that each such confidence factor is in the range [0, 1]            with 0 being the lowest and 1 being the highest. Further            note that a confidence factor here can be raised as well as            lowered depending on how well the target loc sig matches or            is consistent with the population of loc sigs to which it is            compared. Thus, the confidence in any particular verified            loc sig, LS, can fluctuate with successive invocations of            this program if the input to the successive invocations are            with location information geographically “near” LS.        -   (29.5) remove older verified loc sigs from use whose            confidence value is below a predetermined threshold. Note,            it is intended that such predetermined thresholds be            substantially automatically adjustable by periodically            testing various confidence factor thresholds in a specified            geographic area to determine how well the eligible data base            loc sigs (for different thresholds) perform in agreeing with            a number of verified loc sigs in a “loc sig test-bed”,            wherein the test bed may be composed of, for example,            repeatable loc sigs and recent random verified loc sigs.            -   Note that this program may be invoked with a                (verified/known) random and/or repeatable loc sig as                input. Furthermore, the target loc sigs to be updated                may be selected from a particular group of loc sigs such                as the random loc sigs or the repeatable loc sigs, such                selection being determined according to the input                parameter, “selection_criteria” while the comparison                population may be designated with the input parameter,                “loc_sig_pop”. For example, to update confidence factors                of certain random loc sigs near “new_loc_obj”,                “selection_criteria” may be given a value indicating,                “USE_RANDOM_LOC_SIGS”, and “loc_sig_pop” may be given a                value indicating, “USE_REPEATABLE_LOC_SIGS”. Thus, if in                a given geographic area, the repeatable loc sigs (from,                e.g., stationary transceivers) in the area have recently                been updated, then by successively providing                “new_loc_obj” with a loc sig for each of these                repeatable loc sigs, the stored random loc sigs can have                their confidences adjusted.            -   Alternatively, in one embodiment of the present                disclosure, the present function may be used for                determining when it is desirable to update repeatable                loc sigs in a particular area (instead of automatically                and periodically updating such repeatable loc sigs). For                example, by adjusting the confidence factors on                repeatable loc sigs here provides a method for                determining when repeatable loc sigs for a given area                should be updated. That is, for example, when the area's                average confidence factor for the repeatable loc sigs                drops below a given (potentially high) threshold, then                the MSs that provide the repeatable loc sigs can be                requested to respond with new loc sigs for updating the                data base. Note, however, that the approach presented in                this function assumes that the repeatable location                information in the location signature data base 1320 is                maintained with high confidence by, for example,                frequent data base updating. Thus, the random location                signature data base verified location information may be                effectively compared against the repeatable loc sigs in                an area.        -   Input:            -   new_loc_obj: a data representation at least including a                loc sig for an associated location about which Location                Signature loc sigs are to have their confidences                updated.            -   selection_criteria: a data representation designating                the loc sigs to be selected to have their confidences                updated (may be defaulted). The following groups of loc                sigs may be selected: “USE_RANDOM_LOC_SIGS” (this is the                default), USE_REPEATABLE_LOC_SIGS”, “USE_ALL_LOC_SIGS”.                Note that each of these selections has values for the                following values associated with it (although the values                may be defaulted):                -   (a) a confidence reduction factor for reducing loc                    sig confidences,                -   (b) a big_error_threshold for determining the errors                    above which are considered too big to ignore,                -   (c) a confidence increase factor for increasing loc                    sig confidences,                -   (d) a small error threshold for determining the                    errors below which are considered too small (i.e.,                    good) to ignore.                -   (e) a recent time for specifying a time period for                    indicating the loc sigs here considered to be                    “recent”. loc_sig_pop: a data representation of the                    type of loc sig population to which the loc sigs to                    be updated are compared. The following values may be                    provided:                -   (a) “USE ALL LOC SIGS IN DB”,                -   (b) “USE ONLY REPEATABLE LOC SIGS” (this is the                    default),                -   (c) “USE ONLY LOC SIGS WITH SIMILAR TIME OF DAY”            -    However, environmental characteristics such as:                weather, traffic, season are also contemplated.                Confidence Aging Program

The following program reduces the confidence of verified loc sigs in thelocation signature data base 1320 that are likely to be no longeraccurate (i.e., in agreement with comparable loc sigs in the data base).If the confidence is reduced low enough, then such loc sigs are removedfrom the data base. Further, if for a location signature data baseverified location composite entity (i.e., a collection of loc sigs forthe same location and time), this entity no longer references any validloc sigs, then it is also removed from the data base. Note that thisprogram is invoked by “Update_Loc_Sig_DB”.

-   reduce_bad_DB_loc_sigs(loc_sig_bag, error_rec_set,    big_error_threshold confidence_reduction_factor, recent_time)    Inputs:    -   loc_sig_bag: A collection or “bag” of loc sigs to be tested for        determining if their confidences should be lowered and/or any of        these loc sigs removed.    -   error_rec_set: A set of error records (objects), denoted        “error_recs”, providing information as to how much each loc sig        in “loc_sig_bag” disagrees with comparable loc sigs in the data        base. That is, there is a “error_rec” here for each loc sig in        “loc_sig_bag”.    -   big_error_threshold: The error threshold above which the errors        are considered too big to ignore.    -   confidence_reduction_factor: The factor by which to reduce the        confidence of loc sigs.    -   recent_time: Time period beyond which loc sigs are no longer        considered recent. Note that “recent” loc sigs (i.e., more        recent than “recent_time”) are not subject to the confidence        reduction and filtering of this actions of this function.        Confidence Enhancement Program

The following program increases the confidence of verified LocationSignature loc sigs that are (seemingly) of higher accuracy (i.e., inagreement with comparable loc sigs in the location signature data base1320). Note that this program is invoked by “Update_Loc_Sig_DB”.

-   increase_confidence_of_good_DB_Ioc_sigs(nearby_loc_sig_bag,    error_rec_set, small_error_threshold, confidence_increase_factor,    recent_time);    Inputs:    -   loc_sig_bag: A collection or “bag” of to be tested for        determining if their confidences should be increased.    -   error_rec_set: A set of error records (objects), denoted        “error_recs”, providing information as to how much each loc sig        in “loc_sig_bag” disagrees with comparable loc sigs in the        location signature data base. That is, there is a “error_rec”        here for each loc sig in “loc_sig_bag”.    -   small_error_threshold: The error threshold below which the        errors are considered too small to ignore.    -   confidence_increase_factor: The factor by which to increase the        confidence of loc sigs.    -   recent_time: Time period beyond which loc sigs are no longer        considered recent. Note that “recent” loc sigs (i.e., more        recent than “recent_time”) are not subject to the confidence        reduction and filtering of this actions of this function.        Location Hypotheses Consistency Program

The following program determines the consistency of location hypotheseswith verified location information in the location signature data base1320. Note that in the one embodiment of the present disclosure, thisprogram is invoked primarily by a module denoted the historical locationreasoner 1424 described sections hereinbelow. Moreover, the detaileddescription for this program is provided with the description of thehistorical location reasoner hereinbelow for completeness.

DB_Loc_Sig_Error_Fit(hypothesis, measured_loc_sig_bag, search_criteria)

-   -   /* This function determines how well the collection of loc sigs        in “measured_loc_sig_bag” fit with the loc sigs in the location        signature data base 1320 wherein the data base loc sigs must        satisfy the criteria of the input parameter “search_criteria”        and are relatively close to the MS location estimate of the        location hypothesis, “hypothesis”.        -   Input:            -   hypothesis: MS location hypothesis;            -   measured_loc_sig_bag: A collection of measured location                signatures (“loc sigs” for short) obtained from the MS                (the data structure here is an aggregation such as an                array or list). Note, it is assumed that there is at                most one loc sig here per Base Station in this                collection. Additionally, note that the input data                structure here may be a location signature cluster such                as the “loc_sig_cluster” field of a location hypothesis                (cf. FIGS. 9A and B). Note that variations in input data                structures may be accepted here by utilization of flag                or tag bits as one skilled in the art will appreciate;            -   search_criteria: The criteria for searching the verified                location signature data base for various categories of                loc sigs. The only limitation on the types of categories                that may be provided here is that, to be useful, each                category should have meaningful number of loc sigs in                the location signature data base. The following                categories included here are illustrative, but others                are contemplated:                -   (a) “USE ALL LOC SIGS IN DB” (the default),                -   (b) “USE ONLY REPEATABLE LOC SIGS”,                -   (c) “USE ONLY LOC SIGS WITH SIMILAR TIME OF DAY”.            -    Further categories of loc sigs close to the MS estimate                of “hypothesis” contemplated are: all loc sigs for the                same season and same time of day, all loc sigs during a                specific weather condition (e.g., snowing) and at the                same time of day, as well as other limitations for other                environmental conditions such as traffic patterns. Note,                if this parameter is NIL, then (a) is assumed.        -   Returns: An error object (data type: “error_object”)            having: (a) an “error” field with a measurement of the error            in the fit of the location signatures from the MS with            verified location signatures in the location signature data            base 1320; and (b) a “confidence” field with a value            indicating the perceived confidence that is to be given to            the “error” value. */Location            Signature Comparison Program

The following program compares: (a1) loc sigs that are contained in (orderived from) the loc sigs in “target_loc_sig_bag” with (b1) loc sigscomputed from verified loc sigs in the location signature data base1320. That is, each loc sig from (a1) is compared with a correspondingloc sig from (b1) to obtain a measurement of the discrepancy between thetwo loc sigs. In particular, assuming each of the loc sigs for“target_loc_sig_bag” correspond to the same target MS location, whereinthis location is “target_loc”, this program determines how well the locsigs in “target_loc_sig_bag” fit with a computed or estimated loc sigfor the location, “target_loc” that is derived from the verified locsigs in the location signature data base 1320. Thus, this program may beused: (a2) for determining how well the loc sigs in the locationsignature cluster for a target MS (“target_loc_sig_bag”) compares withloc sigs derived from verified location signatures in the locationsignature data base, and (b2) for determining how consistent a givencollection of loc sigs (“target_loc_sig_bag”) from the locationsignature data base is with other loc sigs in the location signaturedata base. Note that in (b2) each of the one or more loc sigs in“target_loc_sig_bag” have an error computed here that can be used indetermining if the loc sig is becoming inapplicable for predictingtarget MS locations.

-   Determine_Location_Signature_Fit_Errors(target_loc,    target_loc_sig_bag, search_area, search_criteria, output_criteria)    -   /* Input:        -   target_loc: An MS location or a location hypothesis for an            MS. Note, this can be any of the following:            -   (a) An MS location hypothesis, in which case, if the                hypothesis is inaccurate, then the loc sigs in                “target_loc_sig_bag” are the location signature cluster                from which this location hypothesis was derived. Note                that if this location is inaccurate, then                “target_loc_sig_bag” is unlikely to be similar to the                comparable loc sigs derived from the loc sigs of the                location signature data base close “target_loc”; or            -   (b) A previously verified MS location, in which case,                the loc sigs of “target_loc_sig_bag” were the loc sigs                measurements at the time they were verified. However,                these loc sigs may or may not be accurate now.        -   target_loc_sig_bag: Measured location signatures (“loc sigs”            for short) obtained from the MS (the data structure here,            bag, is an aggregation such as array or list). It is assumed            that there is at least one loc sig in the bag. Further, it            is assumed that there is at most one loc sig per Base            Station;        -   search_area: The representation of the geographic area            surrounding “target_loc”. This parameter is used for            searching the Location Signature data base for verified loc            sigs that correspond geographically to the location of an MS            in “search_area;        -   search_criteria: The criteria used in searching the location            signature data base. The criteria may include the following:            -   (a) “USE ALL LOC SIGS IN DB”,            -   (b) “USE ONLY REPEATABLE LOC SIGS”,            -   (c) “USE ONLY LOC SIGS WITH SIMILAR TIME OF DAY”.        -    However, environmental characteristics such as: weather,            traffic, season are also contemplated.        -   output_criteria: The criteria used in determining the error            records to output in “error_rec_bag”. The criteria here may            include one of:            -   (a) “OUTPUT ALL POSSIBLE ERROR_RECS”;            -   (b) “OUTPUT ERROR_RECS FOR INPUT LOC SIGS ONLY”.    -   Returns: error_rec_bag: A bag of error records or objects        providing an indication of the similarity between each loc sig        in “target_loc_sig_bag” and an estimated loc sig computed for        “target_loc” from stored loc sigs in a surrounding area of        “target_loc”. Thus, each error record/object in “error_rec_bag”        provides a measurement of how well a loc sig (i.e., wireless        signal characteristics) in “target_loc_sig_bag” (for an        associated BS and the MS at “target_loc”) correlates with an        estimated loc sig between this BS and MS. Note that the        estimated loc sigs are determined using verified location        signatures in the Location Signature data base. Note, each error        record in “error_rec_bag” includes: (a) a BS ID indicating the        base station to which the error record corresponds; and (b) a        error measurement (>=0), and (c) a confidence value (in [0, 1])        indicating the confidence to be placed in the error measurement.        Computed Location Signature Program

The following program receives a collection of loc sigs and computes aloc sig that is representative of the loc sigs in the collection. Thatis, given a collection of loc sigs, “loc_sig_bag”, wherein each loc sigis associated with the same predetermined Base Station, this programuses these loc sigs to compute a representative or estimated loc sigassociated with the predetermined Base Station and associated with apredetermined MS location, “loc_for_estimation”. Thus, if the loc sigsin “loc_sig_bag” are from the verified loc sigs of the locationsignature data base such that each of these loc sigs also has itsassociated MS location relatively close to “loc_for_estimation”, thenthis program can compute and return a reasonable approximation of what ameasured loc sig between an MS at “loc_for_estimation” and thepredetermined Base Station ought to be. This program is invoked by“Determine_Location_Signature_Fit_Errors”.

-   estimate_loc_sig_from_DB(loc_for_estimation, loc_sig_bag)    Geographic Area Representation Program

The following program determines and returns a representation of ageographic area about a location, “loc”, wherein: (a) the geographicarea has associated MS locations for an acceptable number (i.e., atleast a determined minimal number) of verified loc sigs from thelocation signature data base, and (b) the geographical area is not toobig. However, if there are not enough loc sigs in even a largestacceptable search area about “loc”, then this largest search area isreturned. “DB_Loc_Sig_Error_Fit”

-   get_area_to_search(loc)    Location signature Comparison Program

This program compares two location signatures, “target_loc_sig” and“comparison_loc_sig”, both associated with the same predetermined BaseStation and the same predetermined MS location (or hypothesizedlocation). This program determines a measure of the difference or errorbetween the two loc sigs relative to the variability of the verifiedlocation signatures in a collection of loc sigs denoted the“comparison_loc_sig_bag” obtained from the location signature data base.It is assumed that “target_loc_sig”, “comparison_loc_sig” and the locsigs in “comparison_loc_sig_bag” are all associated with the same basestation. This program returns an error record (object), “error_rec”,having an error or difference value and a confidence value for the errorvalue. Note, the signal characteristics of “target_loc_sig” and those of“comparison_loc_sig” are not assumed to be similarly normalized (e.g.,via filters as per the filters of the Signal Processing Subsystem) priorto entering this function. It is further assumed that typically theinput loc sigs satisfy the “search_criteria”. This program is invokedby: the program, “Determine_Location_Signature_Fit_Errors”, describedabove.

-   get_difference_measurement(target_loc_sig, comparison_loc_sig,    comparison_loc_sig_bag, search_area, search_criteria)

Input:

-   -   target_loc_sig: The loc sig to which the “error_rec” determined        here is to be associated.    -   comparison_loc_sig: The loc sig to compare with the        “target_loc_sig”. Note, if “comparison_loc_sig” is NIL, then        this parameter has a value that corresponds to a noise level of        “target_loc_sig”.    -   comparison_loc_sig_bag: The universe of loc sigs to use in        determining an error measurement between “target_loc_sig” and        “comparison_loc_sig”. Note, the loc sigs in this aggregation        include all loc sigs for the associated BS that are in the        “search_area”.    -   search_area: A representation of the geographical area        surrounding the location for all input loc sigs. This input is        used for determining extra information about the search area in        problematic circumstances.    -   search_criteria: The criteria used in searching the location        signature data base. The criteria may include the following:        -   (a) “USE ALL LOC SIGS IN DB”,        -   (b) “USE ONLY REPEATABLE LOC SIGS”,        -   (c) “USE ONLY LOC SIGS WITH SIMILAR TIME OF DAY    -    However, environmental characteristics such as: weather,        traffic, season are also contemplated.        Detailed Description of the Hypothesis Evaluator Modules        Context Adjuster Embodiments

The context adjuster 1326 performs the first set of potentially manyadjustments to at least the confidences of location hypotheses, and insome important embodiments, both the confidences and the target MSlocation estimates provided by FOMs 1224 may be adjusted according toprevious performances of the FOMs. More particularly, as mentionedabove, the context adjuster adjusts confidences so that, assuming thereis a sufficient density verified location signature clusters captured inthe location signature data base 1320, the resulting location hypothesesoutput by the context adjuster 1326 may be further processed uniformlyand substantially without concern as to differences in accuracy betweenthe first order models from which location hypotheses originate.Accordingly, the context adjuster adjusts location hypotheses both toenvironmental factors (e.g., terrain, traffic, time of day, etc., asdescribed in 30.1 above), and to how predictable or consistent eachfirst order model (FOM) has been at locating previous target MS's whoselocations were subsequently verified.

Of particular importance is the novel computational paradigm utilizedherein. That is, if there is a sufficient density of previous verifiedMS location data stored in the location signature data base 1320, thenthe FOM location hypotheses are used as an “index” into this data base(i.e., the location signature data base) for constructing new target MS140 location estimates. A more detailed discussion of this aspect of thepresent disclosure is given hereinbelow. Accordingly, only a briefoverview is provided here. Thus, since the location signature data base1320 stores previously captured MS location data including:

-   -   (a) clusters of MS location signature signals (see the location        signature data base section for a discussion of these signals)        and    -   (b) a corresponding verified MS location, for each such cluster,        from where the MS signals originated,        the context adjuster 1326 uses newly created target MS location        hypotheses output by the FOM's as indexes or pointers into the        location signature data base for identifying other geographical        areas where the target MS 140 is likely to be located based on        the verified MS location data in the location signature data        base.

In particular, at least the following two criteria are addressed by thecontext adjuster 1326:

-   -   (32.1) Confidence values for location hypotheses are to be        comparable regardless of first order models from which the        location hypotheses originate. That is, the context adjuster        moderates or dampens confidence value assignment distinctions or        variations between first order models so that the higher the        confidence of a location hypothesis, the more likely (or        unlikely, if the location hypothesis indicates an area estimate        where the target MS is NOT) the target MS is perceived to be in        the estimated area of the location hypothesis regardless of the        First Order Model from which the location hypothesis was output;    -   (32.2) Confidence values for location hypotheses may be adjusted        to account for current environmental characteristics such as        month, day (weekday or weekend), time of day, area type (urban,        rural, etc.), traffic and/or weather when comparing how accurate        the first order models have previously been in determining an MS        location according to such environmental characteristics. For        example, in one embodiment of the present disclosure, such        environmental characteristics are accounted for by utilizing a        transmission area type scheme (as discussed in section 5.9        above) when adjusting confidence values of location hypotheses.        Details regarding the use of area types for adjusting the        confidences of location hypotheses and provided hereinbelow, and        in particular, in APPENDIX D.

Note that in satisfying the above two criteria, the context adjuster1326, at least in one embodiment, may use heuristic (fuzzy logic) rulesto adjust the confidence values of location hypotheses from the firstorder models. Additionally, the context adjuster may also satisfy thefollowing criteria:

-   -   (33.1) The context adjuster may adjust location hypothesis        confidences due to BS failure(s),    -   (33.2) Additionally in one embodiment, the context adjuster may        have a calibration mode for at least one of:        -   (a) calibrating the confidence values assigned by first            order models to their location hypotheses outputs;        -   (b) calibrating itself.

A first embodiment of the context adjuster is discussed immediatelyhereinbelow and in APPENDIX D. However, the present disclosure alsoincludes other embodiments of the context adjuster. A second embodimentis also described in Appendix D so as to not overburden the reader andthereby chance losing perspective of the overall invention.

A description of the high level functions in an embodiment of thecontext adjuster 1326 follows. Details regarding the implementation ofthese functions are provided in APPENDIX D. Also, many of the terms usedhereinbelow are defined in APPENDIX D. Accordingly, the programdescriptions in this section provide the reader with an overview of thisfirst embodiment of the context adjuster 1326.

Context_adjuster(loc_hyp_list)

This function adjusts the location hypotheses on the list,“loc_hyp_list”, so that the confidences of the location hypotheses aredetermined more by empirical data than default values from the FirstOrder Models 1224. That is, for each input location hypothesis, itsconfidence (and an MS location area estimate) may be exclusivelydetermined here if there are enough verified location signaturesavailable within and/or surrounding the location hypothesis estimate.

This function creates a new list of location hypotheses from the inputlist, “loc_hyp_list”, wherein the location hypotheses on the new listare modified versions of those on the input list. For each locationhypothesis on the input list, one or more corresponding locationhypotheses will be on the output list. Such corresponding outputlocation hypotheses will differ from their associated input locationhypothesis by one or more of the following: (a) the “image_area” field(see FIGS. 9A and 9B) may be assigned an area indicative of where thetarget MS is estimated to be, (b) if “image_area” is assigned, then the“confidence” field will be the confidence that the target MS is locatedin the area for “image_area”, (c) if there are not sufficient “nearby”verified location signature clusters in the location signature data base1320 to entirely rely on a computed confidence using such verifiedlocation signature clusters, then two location hypotheses (havingreduced confidences) will be returned, one having a reduced computedconfidence (for “image_area”) using the verified clusters in theLocation Signature data base, and one being substantially the same asthe associated input location hypothesis except that the confidence (forthe field “area_est”) is reduced to reflect the confidence in its pairedlocation hypothesis having a computed confidence for “image_area”. Notealso, in some cases, the location hypotheses on the input list, may haveno change to its confidence or the area to which the confidence applies.

Get_adjusted_loc_hyp_list_for(loc_hyp)

This function returns a list (or more generally, an aggregation object)of one or more location hypotheses related to the input locationhypothesis, “loc_hyp”. In particular, the returned location hypotheseson the list are “adjusted” versions of “loc_hyp” in that both theirtarget MS 140 location estimates, and confidence placed in suchestimates may be adjusted according to archival MS location informationin the location signature data base 1320. Note that the steps herein arealso provided in flowchart form in FIGS. 26 a through 26 c.

-   RETURNS: loc_hyp_list This is a list of one or more location    hypotheses related to the input “loc_hyp”. Each location hypothesis    on “loc_hyp_list” will typically be substantially the same as the    input “loc_hyp” except that there may now be a new target MS    estimate in the field, “image_area”, and/or the confidence value may    be changed to reflect information of verified location signature    clusters in the location signature data base.

The function, “get_adjusted_loc_hyp_list_for,” and functions called bythis function presuppose a framework or paradigm that requires somediscussion as well as the defining of some terms. Note that some of theterms defined hereinbelow are illustrated in FIG. 24.

Define the term the “the cluster set” to be the set of all MS locationpoint estimates (e.g., the values of the “pt_est” field of the locationhypothesis data type), for the present FOM, such that:

-   -   (a) these estimates are within a predetermined corresponding        area (e.g., the “loc_hyp.pt_covering” being such a predetermined        corresponding area, or more generally, this predetermined        corresponding area is determined as a function of the distance        from an initial location estimate, e.g., “loc_hyp.pt_est”, from        the FOM), and    -   (b) these point estimates have verified location signature        clusters in the location signature data base.

Note that the predetermined corresponding area above will be denoted asthe “cluster set area”.

Define the term “image cluster set” (for a given First Order Modelidentified by “loc_hyp.FOM_ID”) to mean the set of verified locationsignature clusters whose MS location point estimates are in “the clusterset”.

Note that an area containing the “image cluster set” will be denoted asthe “image cluster set area” or simply the “image area” in somecontexts. Further note that the “image cluster set area” will be a“small” area encompassing the “image cluster set”. In one embodiment,the image cluster set area will be the smallest covering of cells fromthe mesh for the present FOM that covers the convex hull of the imagecluster set. Note that preferably, each cell of each mesh for each FOMis substantially contained within a single (transmission) area type.

Thus, the present FOM provides the correspondences or mapping betweenelements of the cluster set and elements of the image cluster set.

confidence_adjuster(FOM_ID, image_area, image_cluster_set)

This function returns a confidence value indicative of the target MS 140being in the area for “image_area”. Note that the steps for thisfunction are provided in flowchart form in FIGS. 27 a and 27 b.

-   RETURNS: A confidence value. This is a value indicative of the    target MS being located in the area represented by “image_area”    (when it is assumed that for the related “loc_hyp,” the “cluster set    area” is the “loc_hyp.pt_covering” and “loc_hyp.FOM_ID” is    “FOM_ID”).

The function, “confidence_adjuster,” (and functions called by thisfunction) presuppose a framework or paradigm that requires somediscussion as well as the defining of terms.

Define the term “mapped cluster density” to be the number of theverified location signature clusters in an “image cluster set” per unitof area in the “image cluster set area”.

-   -   It is believed that the higher the “mapped cluster density”, the        greater the confidence can be had that a target MS actually        resides in the “image cluster set area” when an estimate for the        target MS (by the present FOM) is in the corresponding “the        cluster set”.    -   Thus, the mapped cluster density becomes an important factor in        determining a confidence value for an estimated area of a target        MS such as, for example, the area represented by “image_area”.        However, the mapped cluster density value requires modification        before it can be utilized in the confidence calculation. In        particular, confidence values must be in the range [−1, 1] and a        mapped cluster density does not have this constraint. Thus, a        “relativized mapped cluster density” for an estimated MS area is        desired, wherein this relativized measurement is in the range        [−1, +1], and in particular, for positive confidences in the        range [0, 1]. Accordingly, to alleviate this difficulty, for the        FOM define the term “prediction mapped cluster density” as a        mapped cluster density value, MCD, for the FOM and image cluster        set area wherein:    -   (i) MCD is sufficiently high so that it correlates (at least at        a predetermined likelihood threshold level) with the actual        target MS location being in the “image cluster set area” when a        FOM target MS location estimate is in the corresponding “cluster        set area”;    -   That is, for a cluster set area (e.g., “loc_hyp.pt_covering”)        for the present FOM, if the image cluster set area: has a mapped        cluster density greater than the “prediction mapped cluster        density”, then there is a high likelihood of the target MS being        in the image cluster set area.    -   It is believed that the prediction mapped cluster density will        typically be dependent on one or more area types. In particular,        it is assumed that for each area type, there is a likely range        of prediction mapped cluster density values that is        substantially uniform across the area type. Accordingly, as        discussed in detail hereinbelow, to calculate a prediction        mapped cluster density for a particular area type, an estimate        is made of the correlation between the mapped cluster densities        of image areas (from cluster set areas) and the likelihood that        if a verified MS location: (a) has a corresponding FOM MS        estimate in the cluster set, and (b) is also in the particular        area type, then the verified MS location is also in the image        area.    -   Thus, if an area is within a single area type, then such a        “relativized mapped cluster density” measurement for the area        may be obtained by dividing the mapped cluster density by the        prediction mapped cluster density and taking the smaller of: the        resulting ratio and 1.0 as the value for the relativized mapped        cluster density.    -   In some (perhaps most) cases, however, an area (e.g., an image        cluster set area) may have portions in a number of area types.        Accordingly, a “composite prediction mapped cluster density” may        be computed, wherein, a weighted sum is computed of the        prediction mapped cluster densities for the portions of the area        that is in each of the area types. That is, the weighting, for        each of the single area type prediction mapped cluster        densities, is the fraction of the total area that this area type        is. Thus, a “relativized composite mapped cluster density” for        the area here may also be computed by dividing the mapped        cluster density by the composite prediction mapped cluster        density and taking the smaller of: the resulting ratio and 1.0        as the value for the relativized composite mapped cluster        density.        Accordingly, note that as such a relativized (composite) mapped        cluster density for an image cluster set area        increases/decreases, it is assumed that the confidence of the        target MS being in the image cluster set area should        increase/decrease, respectively.

-   get_composite_prediction_mapped_cluster_density_for_high_certainty(FOM_ID,    image_area);

The present function determines a composite prediction mapped clusterdensity by determining a composite prediction mapped cluster density forthe area represented by “image_area” and for the First Order Modelidentified by “FOM_ID”.

-   -   OUTPUT: composite_mapped_density This is a record for the        composite prediction mapped cluster density. In particular,        there are with two fields:        -   (i) a “value” field giving an approximation to the            prediction mapped cluster density for the First Order Model            having id, FOM_ID;        -   (ii) a “reliability” field giving an indication as to the            reliability of the “value” field. The reliability field is            in the range [0, 1] with 0 indicating that the “value” field            is worthless and the larger the value the more assurance can            be put in “value” with maximal assurance indicated when            “reliability” is 1.

-   get_prediction_mapped_cluster_density_for(FOM_ID, area_type)

The present function determines an approximation to a prediction mappedcluster density, D, for an area type such that if an image cluster setarea has a mapped cluster density >=D, then there is a high expectationthat the target MS 140 is in the image cluster set area. Note that thereare a number of embodiments that may be utilized for this function. Thesteps herein are also provided in flowchart form in FIGS. 29 a through29 h.

-   OUTPUT: prediction_mapped_cluster_density This is a value giving an    approximation to the prediction mapped cluster density for the First    Order Model having identity, “FOM_ID”, and for the area type    represented by “area_type”*/

It is important to note that the computation here for the predictionmapped cluster density may be more intense than some other computationsbut the cluster densities computed here need not be performed in realtime target MS location processing. That is, the steps of this functionmay be performed only periodically (e.g., once a week), for each FOM andeach area type thereby precomputing the output for this function.Accordingly, the values obtained here may be stored in a table that isaccessed during real time target MS location processing. However, forsimplicity, only the periodically performed steps are presented here.However, one skilled in the art will understand that with sufficientlyfast computational devices, some related variations of this function maybe performed in real-time. In particular, instead of supplying area typeas an input to this function, a particular area, A, may be provided suchas the image area for a cluster set area, or, the portion of such animage area in a particular area type. Accordingly, wherever “area_type”is used in a statement of the embodiment of this function below, acomparable statement with “A” can be provided.

Location Hypothesis Analyzer Embodiment

Referring now to FIG. 7, an embodiment of the Hypothesis Analyzer isillustrated. The control component is denoted the control module 1400.Thus, this control module manages or controls access to the run timelocation hypothesis storage area 1410. The control module 1400 and therun time location hypothesis storage area 1410 may be implemented as ablackboard system and/or an expert system. Accordingly, in theblackboard embodiment, and the control module 1400 determines when newlocation hypotheses may be entered onto the blackboard from otherprocesses such as the context adjuster 1326 as well as when locationhypotheses may be output to the most likelihood estimator 1344.

The following is a brief description of each submodule included in thelocation hypothesis analyzer 1332.

-   (35.1) A control module 1400 for managing or controlling further    processing of location hypotheses received from the context    adjuster. This module controls all location hypothesis processing    within the location hypothesis analyzer as well as providing the    input interface with the context adjuster. There are numerous    embodiments that may be utilized for this module, including, but not    limited to, expert systems and blackboard managers.-   (35.2) A run-time location hypothesis storage area 1410 for    retaining location hypotheses during their processing by the    location hypotheses analyzer. This can be, for example, an expert    system fact base or a blackboard. Note that in some of the    discussion hereinbelow, for simplicity, this module is referred to    as a “blackboard”. However, it is not intended that such notation be    a limitation on the present disclosure; i.e., the term “blackboard”    hereinafter will denote a run-time data repository for a data    processing paradigm wherein the flow of control is substantially    data-driven.-   (35.3) An analytical reasoner module 1416 for determining if (or how    well) location hypotheses are consistent with well known physical or    heuristic constraints as, e.g., mentioned in (30.4) above. Note that    this module may be a daemon or expert system rule base.-   (35.4) An historical location reasoner module 1424 for adjusting    location hypotheses' confidences according to how well the location    signature characteristics (i.e., loc sigs) associated with a    location hypothesis compare with “nearby” loc sigs in the location    signature data base as indicated in (30.3) above. Note that this    module may also be a daemon or expert system rule base.-   (35.5) A location extrapolator module 1432 for use in updating    previous location estimates for a target MS when a more recent    location hypothesis is provided to the location hypothesis analyzer    1332. That is, assume that the control module 1400 receives a new    location hypothesis for a target MS for which there are also one or    more previous location hypotheses that either have been recently    processed (i.e., they reside in the MS status repository 1338, as    shown best in FIG. 6), or are currently being processed (i.e., they    reside in the run-time location hypothesis storage area 1410).    Accordingly, if the active_timestamp (see FIGS. 9A and 9B regarding    location hypothesis data fields) of the newly received location    hypothesis is sufficiently more recent than the active_timestamp of    one of these previous location hypotheses, then an extrapolation may    be performed by the location extrapolator module 1432 on such    previous location hypotheses so that all target MS location    hypotheses being concurrently analyzed are presumed to include    target MS location estimates for substantially the same point in    time. Thus, initial location estimates generated by the FOMs using    different wireless signal measurements, from different signal    transmission time intervals, may have their corresponding dependent    location hypotheses utilized simultaneously for determining a most    likely target MS location estimate. Note that this module may also    be daemon or expert system rule base.-   (35.6) hypothesis generating module 1428 for generating additional    location hypotheses according to, for example, MS location    information not adequately utilized or modeled. Note, location    hypotheses may also be decomposed here if, for example it is    determined that a location hypothesis includes an MS area estimate    that has subareas with radically different characteristics such as    an MS area estimate that includes an uninhabited area and a densely    populated area. Additionally, the hypothesis generating module 1428    may generate “poor reception” location hypotheses that specify MS    location areas of known poor reception that are “near” or intersect    currently active location hypotheses. Note, that these poor    reception location hypotheses may be specially tagged (e.g., with a    distinctive FOM_ID value or specific tag field) so that regardless    of substantially any other location hypothesis confidence value    overlapping such a poor reception area, such an area will maintain a    confidence value of “unknown” (i.e., zero). Note that substantially    the only exception to this constraint is location hypotheses    generated from mobile base stations 148. Note that this module may    also be daemon or expert system rule base.

In the blackboard system embodiment of the location hypothesis analyzer,a blackboard system is the mechanism by which the last adjustments areperformed on location hypotheses and by which additional locationhypotheses may be generated. Briefly, a blackboard system can bedescribed as a particular class of software that typically includes atleast three basic components. That is:

-   -   (36.1) a data base called the “blackboard,” whose stored        information is commonly available to a collection of programming        elements known as “daemons” (described immediately below),        wherein, e.g., in a wireless location system according to the        present disclosure, the blackboard includes information        concerning the current status of the location hypotheses being        evaluated to determine a “most likely” MS location estimate.        Note that this data base is provided by the run time location        hypothesis storage area 1410;    -   (36.2) one or more active (and typically opportunistic)        knowledge sources, denoted conventionally as “daemons,” that        create and modify the contents of the blackboard. The daemons        for determining wireless locations should have application        knowledge specific to wirelessly locating mobile stations. As        shown in FIG. 7, the knowledge sources or daemons in the        hypothesis analyzer include the analytical reasoner module 1416,        the hypothesis generating module 1428, and the historical        location reasoner module 1416;    -   (36.3) a control module that enables the realization of the        behavior in a serial computing environment. The control element        orchestrates the flow of control between the various daemons.        This control module is provided by the control module 1400.

Note that this blackboard system may be commercial, however, theknowledge sources, i.e., daemons, are specifically for wirelesslylocating mobile stations. For further information regarding suchblackboard systems, the following references are incorporated herein byreference: (a) Jagannathan, V., Dodhiawala, R., & Baum, L. S. (1989).Blackboard architectures and applications. Boston, Mass.: Harcourt BraceJovanovich Publishers; (b) Engelmore, R., & Morgan, T. (1988).Blackboard systems. Reading, Mass.: Addison-Wesley Publishing Company.

Alternatively, the control module 1400 and the run-time locationhypothesis storage area 1410 may be implemented as an expert system oras a fuzzy rule inferencing system, wherein the control module 1400activates or “fires” rules related to the knowledge domain (in thepresent case, rules relating to the accuracy of MS location hypothesisestimates), and wherein the rules provide a computational embodiment of,for example, constraints and heuristics related to the accuracy of MSlocation estimates. Thus, the control module 1400 for the presentembodiment is also used for orchestrating, coordinating and controllingthe activity of the individual rule bases of the location hypothesisanalyzer (e.g. as shown in FIG. 7, the analytical reasoner module 1416,the hypothesis generating module 1428, the historical location reasonermodule 1424, and the location extrapolator module 1432). For furtherinformation regarding such expert systems, the following reference isincorporated herein by reference: Waterman, D. A. (1970). A guide toexpert systems. Reading, Mass.: Addison-Wesley Publishing Company.

MS Status Repository Embodiment

The MS status repository 1338 is a run-time storage manager for storinglocation hypotheses from previous activations of the location engine 139(as well as the output target MS location estimate(s)) so that a targetMS may be tracked using target MS location hypotheses from previouslocation engine 139 activations to determine, for example, a movement ofthe target MS between evaluations of the target MS location. Thus, byretaining a moving window of previous location hypotheses used inevaluating positions of a target MS, measurements of the target MS'svelocity, acceleration, and likely next position may be determined bythe location hypothesis analyzer 1332. Further, by providingaccessibility to recent MS location hypotheses, these hypotheses may beused to resolve conflicts between hypotheses in a current activation forlocating the target MS; e.g., MS paths may be stored here for use inextrapolating a new location

Most Likelihood Estimator Embodiment

The most likelihood estimator 1344 is a module for determining a “mostlikely” location estimate for a target MS 140 being located (e.g., as in(30.7) above). In one embodiment, the most likelihood estimator performsan integration or summing of all location hypothesis confidence valuesfor any geographic region(s) of interest having at least one locationhypothesis that has been provided to the most likelihood estimator, andwherein the location hypothesis has a relatively (or sufficiently) highconfidence. That is, the most likelihood estimator 1344 determines thearea(s) within each such region having high confidences (or confidencesabove a threshold) as the most likely target MS 140 location estimates.

In one embodiment of the most likelihood estimator 1344, this moduleutilizes an area mesh, M, over which to integrate, wherein the meshcells of M are preferably smaller than the greatest location accuracydesired. That is, each cell, c, of M is assigned a confidence valueindicating a likelihood that the target MS 140 is located in c, whereinthe confidence value for c is determined by the confidence values of thetarget MS location estimates provided to the most likelihood estimator1344. Thus, to obtain the most likely location determination(s) thefollowing steps are performed:

-   -   (a) For each of the active location hypotheses output by, e.g.,        the hypothesis analyzer 1332 (alternatively, the context        adjuster 1326), each corresponding MS location area estimate,        LAE, is provided with a smallest covering, C_(LEA), of cells c        from M.    -   (b) Subsequently, each of the cells of C_(LEA) have their        confidence values adjusted by adding to it the confidence value        for LAE. Accordingly, if the confidence of LEA is positive, then        the cells of C_(LEA) have their confidences increased.        Alternatively, if the confidence of LEA is negative, then the        cells of C_(LEA) have their confidences decreased.    -   (c) Given that the interval [−1.0, +1.0] represents the range in        confidence values, and that this range has been partitioned into        intervals, Int, having lengths of, e.g., 0.05, for each        interval, Int, perform a cluster analysis function for        clustering cells with confidences that are in Int. Thus, a        topographical-type map may be constructed from the resulting        cell clusters, wherein higher confidence areas are analogous to        representations of areas having higher elevations.    -   (d) Output a representation of the resulting clusters for each        Int to the output gateway 1356 for determining the location        granularity and representation desired by each location        application 146 requesting the location of the target MS 140.

Of course, variations in the above algorithm are also within the scopeof the present disclosure. For example, some embodiments of the mostlikelihood estimator 1344 may:

-   -   (e) Perform special processing for areas designated as “poor        reception” areas. For example, the most likelihood estimator        1344 may be able to impose a confidence value of zero (i.e.,        meaning it is unknown as to whether the target MS is in the        area) on each such poor reception area regardless of the        location estimate confidence values unless there is a location        hypothesis from a reliable and unanticipated source. That is,        the mesh cells of a poor reception area may have their        confidences set to zero unless, e.g., there is a location        hypothesis derived from target MS location data provided by a        mobile base station 148 that: (a) is near the poor reception        area, (b) able to detect that the target MS 140 is in the poor        reception area, and (c) can relay target MS location data to the        location center 142. In such a case, the confidence of the        target MS location estimate from the MBS location hypothesis may        take precedence. Thus, as disclosed, e.g., in the Summary        section hereabove as well as the mobile station descriptions        hereinbelow, such a target MS location estimate from a MBS 148        can be dependent on the GPS location of the MBS 148.    -   (f) Additionally, in some embodiments of the most likelihood        estimator 1344, cells c of M that are “near” or adjacent to a        covering C_(LEA) may also have their confidences adjusted        according to how near the cells c are to the covering. That is,        the assigning of confidences to cell meshes may be “fuzzified”        in the terms of fuzzy logic so that the confidence value of each        location hypothesis utilized by the most likelihood estimator        1344 is provided with a weighting factor depending on its        proxity to the target MS location estimate of the location        hypothesis. More precisely, it is believed that “nearness,” in        the present context, should be monotonic with the “wideness” of        the covering; i.e., as the extent of the covering increases        (decreases) in a particular direction, the cells c affected        beyond the covering also increases (decreases). Furthermore, in        some embodiments of the most likelihood estimator 1344, the        greater (lesser) the confidence in the LEA, the more (fewer)        cells c beyond the covering have their confidences affected. To        describe this technique in further detail, reference is made to        FIG. 10, wherein an area A is assumed to be a covering C_(LEA)        having a confidence denoted “conf”. Accordingly, to determine a        confidence adjustment to add to a cell c not in A (and        additionally, the centroid of A not being substantially        identical with the centroid of c which could occur if A were        donut shaped), the following steps may be performed:        -   (i) Determine the centroid of A, denoted Cent(A).        -   (ii) Determine the centroid of the cell c, denoted Q.        -   (iii) Determine the extent of A along the line between            Cent(A) and Q, denoted L.        -   (iv) For a given type of probability density function, P(x),            such as a Gaussian function, let T be the beginning portion            of the function that lives on the x-axis interval [0, t],            wherein P(t)=ABS(conf)=the absolute value of the confidence            of C_(LEA).        -   (v) Stretch T along the x-axis so that the stretched            function, denoted sT(x), has an x-axis support of [0,            L/(1+e^(−[a(ABS(conf)−1)]))], where a is in range of 3.0 to            10.0; e.g., 5.0. Note that sT(x) is the function,            -   P(x*(1+e^(−[a(ABS(conf)−1)]))/L), on this stretched                extent. Further note that for confidences of +1 and −1,                the support of sT(x) is [0, L] and for confidences at                (or near) zero this support. Further, the term,                L/(1+e ^(−[a(ABS(conf)−1)]))        -    is monotonically increasing with L and ABS(conf).        -   (vi) Determine D=the minimum distance that Q is outside of A            along the line between Cent(A) and Q.        -   (vii) Determine the absolute value of the change in the            confidence of c as sT(D).        -   (viii) Provide the value sT(D) with the same sign as conf,            and provide the potentially sign changed value sT(D) as the            confidence of the cell c.

Additionally, in some embodiments, the most likelihood estimator 1344,upon receiving one or more location hypotheses from the hypothesisanalyzer 1332, also performs some or all of the following tasks:

-   -   (37.1) Filters out location hypotheses having confidence values        near zero whenever such location hypotheses are deemed too        unreliable to be utilized in determining a target MS location        estimate. For example, location hypotheses having confidence        values in the range [−0.02, 0.02] may be filtered here;    -   (37.2) Determines the area of interest over which to perform the        integration. In one embodiment, this area is a convex hull        including each of the MS area estimates from the received        location hypotheses (wherein such location hypotheses have not        been removed from consideration by the filtering process of        (37.1));    -   (37.3) Determines, once the integration is performed, one or        more collections of contiguous area mesh cells that may be        deemed a “most likely” MS location estimate, wherein each such        collection includes one or more area mesh cells having a high        confidence value.        Detailed Description of the Location Hypothesis Analyzer        Submodules        Analytical Reasoner Module

The analytical reasoner applies constraint or “sanity” checks to thetarget MS estimates of the location hypotheses residing in the Run-timeLocation Hypothesis Storage Area for adjusting the associated confidencevalues accordingly. In one embodiment, these sanity checks involve“path” information. That is, this module determines if (or how well)location hypotheses are consistent with well known physical constraintssuch as the laws of physics, in an area in which the MS (associated withthe location hypothesis) is estimated to be located. For example, if thedifference between a previous (most likely) location estimate of atarget MS and an estimate by a current location hypothesis requires theMS to:

-   -   (a) move at an unreasonably high rate of speed (e.g., 200 mph),        or    -   (b) move at an unreasonably high rate of speed for an area        (e.g., 80 mph in a corn patch), or    -   (c) make unreasonably sharp velocity changes (e.g., from 60 mph        in one direction to 60 mph in the opposite direction in 4 sec),        then the confidence in the current hypothesis is reduced. Such        path information may be derived for each time series of location        hypotheses resulting from the FOMs by maintaining a window of        previous location hypotheses in the MS status repository 1338.        Moreover, by additionally retaining the “most likely” target MS        location estimates (output by the most likelihood estimator        1344), current location hypotheses may be compared against such        most likely MS location estimates.

The following path sanity checks are incorporated into the computationsof this module. That is:

-   -   (1) do the predicted MS paths generally follow a known        transportation pathway (e.g., in the case of a calculated speed        of greater than 50 miles per hour are the target MS location        estimates within, for example, 0.2 miles of a pathway where such        speed may be sustained); if so (not), then increase (decrease)        the confidence of the location hypotheses not satisfying this        criterion;    -   (2) are the speeds, velocities and accelerations, determined        from the current and past target MS location estimates,        reasonable for the region (e.g., speeds should be less than 60        miles per hour in a dense urban area at 9 am); if so (not), then        increase (decrease) the confidence of those that are        (un)reasonable;    -   (3) are the locations, speeds, velocities and/or accelerations        similar between target MS tracks produced by different FOMs        similar; decrease the confidence of the currently active        location hypotheses that are indicated as “outliers” by this        criterion;    -   (4) are the currently active location hypothesis target MS        estimates consistent with previous predictions of where the        target MS is predicted to be from a previous (most likely)        target MS estimate; if not, then decrease the confidence of at        least those location hypothesis estimates that are substantially        different from the corresponding predictions. Note, however,        that in some cases this may be over ruled. For example, if the        prediction is for an area for which there is Location Base        Station coverage, and no Location Base Station covering the area        subsequently reports communicating with the target MS, then the        predictions are incorrect and any current location hypothesis        from the same FOM should not be decreased here if it is outside        of this Location Base Station coverage area.

Notice from FIG. 7 that the analytical reasoner can access locationhypotheses currently posted on the Run-time Location Hypothesis StorageArea. Additionally, it interacts with the Pathway Database whichcontains information concerning the location of natural transportationpathways in the region (highways, rivers, etc.) and the AreaCharacteristics Database which contains information concerning, forexample, reasonable velocities that can be expected in various regions(for instance, speeds of 80 mph would not be reasonably expected indense urban areas). Note that both speed and direction can be importantconstraints; e.g., even though a speed might be appropriate for an area,such as 20 mph in a dense urban area, if the direction indicated by atime series of related location hypotheses is directly through anextensive building complex having no through traffic routes, then areduction in the confidence of one or more of the location hypothesesmay be appropriate.

One embodiment of the Analytical Reasoner illustrating how suchconstraints may be implemented is provided in the following section.Note, however, that this embodiment analyzes only location hypotheseshaving a non-negative confidence value.

Modules of an embodiment of the analytical reasoner module 1416 areprovided hereinbelow.

Path Comparison Module

The path comparison module 1454 implements the following strategy: theconfidence of a particular location hypothesis is to be increased(decreased) if it is (not) predicting a path that lies along a knowntransportation pathway (and the speed of the target MS is sufficientlyhigh). For instance, if a time series of target MS location hypothesesfor a given FOM is predicting a path of the target MS that lies along aninterstate highway, the confidence of the currently active locationhypothesis for this FOM should, in general, be increased. Thus, at ahigh level the following steps may be performed:

-   -   (a) For each FOM having a currently active location hypothesis        in the Run-time Location Hypothesis Storage Area (also denoted        “blackboard”), determine a recent “path” obtained from a time        series of location hypotheses for the FOM. This computation for        the “path” is performed by stringing together successive “center        of area” (COA) or centroid values determined from the most        pertinent target MS location estimate in each location        hypothesis (recall that each location hypothesis may have a        plurality of target MS area estimates with one being the most        pertinent). The information is stored in, for example, a matrix        of values wherein one dimension of the matrix identifies the FOM        and a second dimension of the matrix represents a series of COA        path values. Of course, some entries in the matrix may be        undefined.    -   (b) Compare each path obtained in (a) against known        transportation pathways in an area containing the path. A value,        path_match(i), representing to what extent the path matches any        known transportation pathway is computed. Such values are used        later in a computation for adjusting the confidence of each        corresponding currently active location hypothesis.        Velocity/Acceleration Calculation Module

The velocity/acceleration calculation module 1458 computes velocityand/or acceleration estimates for the target MS 140 using currentlyactive location hypotheses and previous location hypothesis estimates ofthe target MS. In one embodiment, for each FOM 1224 having a currentlyactive location hypothesis (with positive confidences) and a sufficientnumber of previous (reasonably recent) target MS location hypotheses, avelocity and/or acceleration may be calculated. In an alternativeembodiment, such a velocity and/or acceleration may be calculated usingthe currently active location hypotheses and one or more recent “mostlikely” locations of the target MS output by the location engine 139. Ifthe estimated velocity and/or acceleration corresponding to a currentlyactive location hypothesis is reasonable for the region, then itsconfidence value may be incremented; if not, then its confidence may bedecremented. The algorithm may be summarized as follows:

-   -   (a) Approximate speed and/or acceleration estimates for        currently active target MS location hypotheses may be provided        using path information related to the currently active location        hypotheses and previous target MS location estimates in a manner        similar to the description of the path comparison module 1454.        Accordingly, a single confidence adjustment value may be        determined for each currently active location hypothesis for        indicating the extent to which its corresponding velocity and/or        acceleration calculations are reasonable for its particular        target MS location estimate. This calculation is performed by        retrieving information from the area characteristics data base        1450 (e.g., FIGS. 6 and 7). Since each location hypothesis        includes timestamp data indicating when the MS location signals        were received from the target MS, the velocity and/or        acceleration associated with a path for a currently active        location hypothesis can be straightforwardly approximated.        Accordingly, a confidence adjustment value, vel_ok(i),        indicating a likelihood that the velocity calculated for the        i^(th) currently active location hypothesis (having adequate        corresponding path information) may be appropriate is calculated        using for the environmental characteristics of the location        hypothesis' target MS location estimate. For example, the area        characteristics data base 1450 may include expected maximum        velocities and/or accelerations for each area type and/or cell        of a cell mesh of the coverage area 120. Thus, velocities and/or        accelerations above such maximum values may be indicative of        anomalies in the MS location estimating process. Accordingly, in        one embodiment, the most recent location hypotheses yielding        such extreme velocities and/or accelerations may have their        confidence values decreased. For example, if the target MS        location estimate includes a portion of an interstate highway,        then an appropriate velocity might correspond to a speed of up        to 100 miles per hour, whereas if the target MS location        estimate includes only rural dirt roads and tomato patches, then        a likely speed might be no more than 30 miles per hour with an        maximum speed of 60 miles per hour (assuming favorable        environmental characteristics such as weather). Note that a list        of such environmental characteristics may include such factors        as: area type, time of day, season. Further note that more        unpredictable environmental characteristics such as traffic flow        patterns, weather (e.g., clear, raining, snowing, etc.) may also        be included, values for these latter characteristics coming from        the environmental data base 1354 which receives and maintains        information on such unpredictable characteristics (e.g., FIGS. 6        and 7). Also note that a similar confidence adjustment value,        acc_ok(i), may be provided for currently active location        hypotheses, wherein the confidence adjustment is related to the        appropriateness of the acceleration estimate of the target MS.        Attribute Comparison Module

The attribute comparison module 1462 compares attribute values forlocation hypotheses generated from different FOMs, and determines if theconfidence of certain of the currently active location hypotheses shouldbe increased due to a similarity in related values for the attribute.That is, for an attribute A, an attribute value for A derived from a setS_(FOM[1]) of one or more location hypotheses generated by one FOM,FOM[1], is compared with another attribute value for A derived from aset S_(FOM2) of one or more location hypotheses generated by a differentFOM, FOM[2] for determining if these attribute values cluster (i.e., aresufficiently close to one another) so that a currently active locationhypothesis in S_(FOM[1]) and a currently active location hypothesis inS_(FOM[2]) should have their confidences increased. For example, theattribute may be a “target MS path data” attribute, wherein a value forthe attribute is an estimated target MS path derived from locationhypotheses generated by a fixed FOM over some (recent) time period.Alternatively, the attribute might be, for example, one of a velocityand/or acceleration, wherein a value for the attribute is a velocityand/or acceleration derived from location hypotheses generated by afixed FOM over some (recent) time period.

In a general context, the attribute comparison module 1462 operatesaccording to the following premise:

-   (38.1) for each of two or more currently active location hypotheses    (with, e.g., positive confidences) if:    -   (a) each of these currently active location hypotheses, H, was        initially generated by a corresponding different FOM_(H);    -   (b) for a given MS estimate attribute and each such currently        active location hypothesis, H, there is a corresponding value        for the attribute (e.g., the attribute value might be an MS path        estimate, or alternatively an MS estimated velocity, or an MS        estimated acceleration), wherein the attribute value is derived        without using a FOM different from FOM_(H), and;    -   (c) the derived attribute values cluster sufficiently well,        then each of these currently active location hypotheses, H, will        have their corresponding confidences increased. That is, these        confidences will be increased by a confidence adjustment value        or delta.

Note that the phrase “cluster sufficiently well” above may have a numberof technical embodiments, including performing various cluster analysistechniques wherein any clusters (according to some statistic) mustsatisfy a system set threshold for the members of the cluster beingclose enough to one another. Further, upon determining the (any)location hypotheses satisfying (38.1), there are various techniques thatmay be used in determining a change or delta in confidences to beapplied. For example, in one embodiment, an initial default confidencedelta that may be utilized is: if “cf” denotes the confidence of such acurrently active location hypothesis satisfying (38.1), then anincreased confidence that still remains in the interval [0, 1.0] may be:cf+[(1−cf)/(1+cf)]², or, cf*[1.0+cf^(n)], n.=>2, or, cf* [a constanthaving a system tuned parameter as a factor]. That is, the confidencedeltas for these examples are: [(1−cf)/(1+cf)]² (an additive delta),and, [1.0+cf^(n)] (a multiplicative delta), and a constant.Additionally, note that it is within the scope of the present disclosureto also provide such confidence deltas (additive deltas ormultiplicative deltas) with factors related to the number of suchlocation hypotheses in the cluster.

Moreover, note that it is an aspect of the present disclosure to providean adaptive mechanism (i.e., the adaptation engine 1382 shown in FIGS.5, 6 and 8) for automatically determining performance enhancing changesin confidence adjustment values such as the confidence deltas for thepresent module. That is, such changes are determined by applying anadaptive mechanism, such as a genetic algorithm, to a collection of“system parameters” (including parameters specifying confidenceadjustment values as well as system parameters of, for example, thecontext adjuster 1326) in order to enhance performance of an embodimentof a novel wireless location system and/or method disclosed herein. Moreparticularly, such an adaptive mechanism may repeatedly perform thefollowing steps:

-   -   (a) modify such system parameters;    -   (b) consequently activate an instantiation of the location        engine 139 (having the modified system parameters) to process,        as input, a series of MS signal location data that has been        archived together with data corresponding to a verified MS        location from which signal location data was transmitted (e.g.,        such data as is stored in the location signature data base        1320); and    -   (c) then determine if the modifications to the system parameters        enhanced location engine 139 performance in comparison to        previous performances.

Assuming this module adjusts confidences of currently active locationhypotheses according to one or more of the attributes: target MS pathdata, target MS velocity, and target MS acceleration, the computationfor this module may be summarized in the following steps:

-   -   (a) Determine if any of the currently active location hypotheses        satisfy the premise (38.1) for the attribute. Note that in        making this determination, average distances and average        standard deviations for the paths (velocities and/or        accelerations) corresponding to currently active location        hypotheses may be computed.    -   (b) For each currently active location hypothesis (wherein “i”        uniquely identifies the location hypothesis) selected to have        its confidence increased, a confidence adjustment value,        path_similar(i) (alternatively, velocity_similar(i) and/or        acceleration_similar(i)), is computed indicating the extent to        which the attribute value matches another attribute value being        predicted by another FOM.

Note that such confidence adjustment values are used later in thecalculation of an aggregate confidence adjustment to particularcurrently active location hypotheses.

Analytical Reasoner Controller

Given one or more currently active location hypotheses for the sametarget MS input to the analytical reasoner controller 1466, thiscontroller activates, for each such input location hypothesis, the othersubmodules of the analytical reasoner module 1416 (denoted hereinafteras “adjustment submodules”) with this location hypothesis. Subsequently,the analytical reasoner controller 1466 receives an output confidenceadjustment value computed by each adjustment submodule for adjusting theconfidence of this location hypothesis. Note that each adjustmentsubmodule determines:

-   -   (a) whether the adjustment submodule may appropriately compute a        confidence adjustment value for the location hypothesis supplied        by the controller. (For example, in some cases there may not be        a sufficient number of location hypotheses in a time series from        a fixed FOM);    -   (b) if appropriate, then the adjustment submodule computes a        non-zero confidence adjustment value that is returned to the        analytical reasoner controller.

Subsequently, the controller uses the output from the adjustmentsubmodules to compute an aggregate confidence adjustment for thecorresponding location hypothesis. In one particular embodiment of thepresent disclosure, values for the eight types of confidence adjustmentvalues (described in sections above) are output to the presentcontroller for computing an aggregate confidence adjustment value foradjusting the confidence of the currently active location hypothesispresently being analyzed by the analytical reasoner module 1416. As anexample of how such confidence adjustment values may be utilized,assuming a currently active location hypothesis is identified by “i”,the outputs from the above described adjustment submodules may be morefully described as:

path_match(i) 1 if there are sufficient previous (and recent) locationhypotheses for the same target MS as “i” that have been generated by thesame FOM that generated “i”, and, the target MS location estimatesprovided by the location hypothesis “i” and the previous locationhypotheses follow a known transportation pathway. 0 otherwise. vel_ok(i)1 if the velocity calculated for the i^(th) currently active locationhypothesis (assuming adequate corresponding path information) is typicalfor the area (and the current environmental characteristics) of thislocation hypothesis' target MS location estimate; 0.2 if the velocitycalculated for the i^(th) currently active location hypothesis is near amaximum for the area (and the current environmental characteristics) ofthis location hypothesis' target MS location estimate;. 0 if thevelocity calculated is above the maximum. ace_ok(i) 1 if theacceleration calculated for the i^(th) currently active locationhypothesis (assuming adequate corresponding path information) is typicalfor the area (and the current environmental characteristics) of thislocation hypothesis' target MS location estimate; 0.2 if theacceleration calculated for the i^(th) currently active locationhypothesis is near a maximum for the area (and the current environmentalcharacteristics) of this location hypothesis' target MS locationestimate;. 0 if the acceleration calculated is above the maximum.similar_path(i) 1 if the location hypothesis “i” satisfies (38.1) forthe target MS path data attribute; 0 otherwise. velocity_similar(i) 1 ifthe location hypothesis “i” satisfies (38.1) for the target MS velocityattribute; 0 otherwise. acceleration_similar(i) 1 if the locationhypothesis “i” satisfies (38.1) for the target MS accelerationattribute; 0 otherwise. extrapolation_chk(i) 1 if the locationhypothesis “i” is “near” a previously predicted MS location for thetarget MS; 0 otherwise.

Additionally, for each of the above confidence adjustments, there is acorresponding location engine 139 system setable parameter whose valuemay be determined by repeated activation of the adaptation engine 1382.Accordingly, for each of the confidence adjustment types, T, above,there is a corresponding system setable parameter, “alpha_T”, that istunable by the adaptation engine 1382. Accordingly, the following highlevel program segment illustrates the aggregate confidence adjustmentvalue computed by the Analytical Reasoner Controller.

target_MS_loc_hyps <--- get all currently active location hypotheses, H,identifying the present target ; for each currently active locationhypothesis, hyp(i), from target_MS_loc_hyps do {   for each of theconfidence adjustment submodules, CA, do     activate CA with hyp(i) asinput;   /* now compute the aggregate confidence adjustment using theoutput    from the confidence adjustment submodules. */  aggregate_adjustment(i) <--- alpha_path_match * path_match(i) +alpha_velocity * vel_ok(i) + alpha_path_similar * path_similar(i) +alpha_velocity_similar * velocity_similar(i) +alpha_acceleration_similar* acceleration_similar(i) +alpha_extrapolation * extrapolation_chk(i);   hyp(i).confidence <---hyp(i).confidence + aggregate_adjustment(i); }Historical Location Reasoner

The historical location reasoner module 1424 may be, for example, adaemon or expert system rule base. The module adjusts the confidences ofcurrently active location hypotheses by using (from location signaturedata base 1320) historical signal data correlated with: (a) verified MSlocations (e.g. locations verified when emergency personnel co-locatewith a target MS location), and (b) various environmental factors toevaluate how consistent the location signature cluster for an inputlocation hypothesis agrees with such historical signal data.

This reasoner will increase/decrease the confidence of a currentlyactive location hypothesis depending on how well its associated loc sigscorrelate with the loc sigs obtained from data in the location signaturedata base.

Note that the embodiment hereinbelow is but one of many embodiments thatmay adjust the confidence of currently active location hypothesesappropriately. Accordingly, it is important to note other embodiments ofthe historical location reasoner functionality are within the scope ofthe present disclosure as one skilled in the art will appreciate uponexamining the techniques utilized within this specification. Forexample, calculations of a confidence adjustment factor may bedetermined using Monte Carlo techniques as in the context adjuster 1326.Each such embodiment generates a measurement of at least one of thesimilarity and the discrepancy between the signal characteristics of theverified location signature clusters in the location signature data baseand the location signature cluster for an input currently activelocation hypothesis, “loc_hyp”.

The embodiment hereinbelow provides one example of the functionalitythat can be provided by the historical location reasoner 1424 (either byactivating the following programs as a daemon or by transforming variousprogram segments into the consequents of expert system rules). Thepresent embodiment generates such a confidence adjustment by thefollowing steps:

-   -   (a) comparing, for each cell in a mesh covering of the most        relevant MS location estimate in “loc_hyp”, the location        signature cluster of the “loc_hyp” with the verified location        signature clusters in the cell so that the following are        computed: (i) a discrepancy or error measurement is determined,        and (ii) a corresponding measurement indicating a likelihood or        confidence of the discrepancy measurement being relatively        accurate in comparison to other such error measurements;    -   (b) computing an aggregate measurement of both the errors and        the confidences determined in (a); and    -   (c) using the computed aggregate measurement of (b) to adjust        the confidence of “loc_hyp”.

The program illustrated in APPENDIX E provides a more detailedembodiment of the steps immediately above.

Location Extrapolator

The location extrapolator 1432 works on the following premise: if for acurrently active location hypothesis there is sufficient previousrelated information regarding estimates of the target MS (e.g., from thesame FOM or from using a “most likely” previous target MS estimateoutput by the location engine 139), then an extrapolation may beperformed for predicting future target MS locations that can be comparedwith new location hypotheses provided to the blackboard. Note thatinterpolation routines (e.g., conventional algorithms such as Lagrangeor Newton polynomials) may be used to determine an equation thatapproximates a target MS path corresponding to a currently activelocation hypothesis.

Subsequently, such an extrapolation equation may be used to compute afuture target MS location. For further information regarding suchinterpolation schemes, the following reference is incorporated herein byreference: Mathews, 1992, Numerical methods for mathematics, science,and engineering. Englewood Cliffs, N.J.: Prentice Hall.

Accordingly, if a new currently active location hypothesis (e.g.,supplied by the context adjuster) is received by the blackboard, thenthe target MS location estimate of the new location hypothesis may becompared with the predicted location. Consequently, a confidenceadjustment value can be determined according to how well the newlocation hypothesis “i” fits with the predicted location. That is, thisconfidence adjustment value will be larger as the new MS estimate andthe predicted estimate become closer together.

Note that in one embodiment of the present disclosure, such predictionsare based solely on previous target MS location estimates output bylocation engine 139. Thus, in such an embodiment, substantially everycurrently active location hypothesis can be provided with a confidenceadjustment value by this module once a sufficient number of previoustarget MS location estimates have been output. Accordingly, a value,extrapolation_chk(i), that represents how accurately the new currentlyactive location hypothesis (identified here by “i”) matches thepredicted location is determined.

Hypothesis Generating Module

The hypothesis generating module 1428 is used for generating additionallocation hypotheses according to, for example, MS location informationnot adequately utilized or modeled. Note, location hypotheses may alsobe decomposed here if, for example it is determined that a locationhypothesis includes an MS area estimate that has subareas with radicallydifferent characteristics such as an area that includes an uninhabitedarea and a densely populated area. Additionally, the hypothesisgenerating module 1428 may generate “poor reception” location hypothesesthat specify MS location areas of known poor reception that are “near”or intersect currently active location hypotheses. Note, that these poorreception location hypotheses may be specially tagged (e.g., with adistinctive FOM_ID value or specific tag field) so that regardless ofsubstantially any other location hypothesis confidence value overlappingsuch a poor reception area, such an area will maintain a confidencevalue of “unknown” (i.e., zero). Note that substantially the onlyexception to this constraint is location hypotheses generated frommobile base stations 148.

Mobile Base Station Location Subsystem Description

Mobile Base Station Subsystem Introduction

Any collection of mobile electronics (denoted mobile location unit) thatis able to both estimate a location of a target MS 140 and communicatewith the base station network may be utilized by an embodiment of anovel wireless location system and/or method disclosed herein to moreaccurately locate the target MS. Such mobile location units may providegreater target MS location accuracy by, for example, homing in on thetarget MS and by transmitting additional MS location information to thelocation center 142. There are a number of embodiments for such a mobilelocation unit contemplated by the present disclosure. For example, in aminimal version, such the electronics of the mobile location unit may belittle more than an onboard MS 140, a sectored/directional antenna and acontroller for communicating between them. Thus, the onboard MS is usedto communicate with the location center 142 and possibly the target MS140, while the antenna monitors signals for homing in on the target MS140. In an enhanced version of the mobile location unit, a GPS receivermay also be incorporated so that the location of the mobile locationunit may be determined and consequently an estimate of the location ofthe target MS may also be determined. However, such a mobile locationunit is unlikely to be able to determine substantially more than adirection of the target MS 140 via the sectored/directional antennawithout further base station infrastructure cooperation in, for example,determining the transmission power level of the target MS or varyingthis power level. Thus, if the target MS or the mobile location unitleaves the coverage area 120 or resides in a poor communication area, itmay be difficult to accurately determine where the target MS is located.None-the-less, such mobile location units may be sufficient for manysituations, and in fact the present disclosure contemplates their use.However, in cases where direct communication with the target MS isdesired without constant contact with the base station infrastructure, asystem or method according to the present disclosure may include amobile location unit that is also a scaled down version of a basestation 122. Thus, given that such a mobile base station or MBS 148includes at least an onboard MS 140, a sectored/directional antenna, aGPS receiver, a scaled down base station 122 and sufficient components(including a controller) for integrating the capabilities of thesedevices, an enhanced autonomous MS mobile location system can beprovided that can be effectively used in, for example, emergencyvehicles, airplanes and boats. Accordingly, the description that followsbelow describes an embodiment of an MBS 148 having the above mentionedcomponents and capabilities for use in a vehicle.

As a consequence of the MBS 148 being mobile, there are fundamentaldifferences in the operation of an MBS in comparison to other types ofBS's 122 (152). In particular, other types of base stations have fixedlocations that are precisely determined and known by the locationcenter, whereas a location of an MBS 148 may be known only approximatelyand thus may require repeated and frequent re-estimating. Secondly,other types of base stations have substantially fixed and stablecommunication with the location center (via possibly other BS's in thecase of LBSs 152) and therefore although these BS's may be more reliablein their in their ability to communicate information related to thelocation of a target MS with the location center, accuracy can beproblematic in poor reception areas. Thus, MBS's may be used in areas(such as wilderness areas) where there may be no other means forreliably and cost effectively locating a target MS 140 (i.e., there maybe insufficient fixed location BS's coverage in an area).

FIG. 11 provides a high level block diagram architecture of oneembodiment of the MBS location subsystem 1508. Accordingly, an MBS mayinclude components for communicating with the fixed location BS networkinfrastructure and the location center 142 via an on-board transceiver1512 that is effectively an MS 140 integrated into the locationsubsystem 1508. Thus, if the MBS 148 travels through an area having poorinfrastructure signal coverage, then the MBS may not be able tocommunicate reliably with the location center 142 (e.g., in rural ormountainous areas having reduced wireless telephony coverage). So it isdesirable that the MBS 148 must be capable of functioning substantiallyautonomously from the location center. In one embodiment, this impliesthat each MBS 148 must be capable of estimating both its own location aswell as the location of a target MS 140.

Additionally, many commercial wireless telephony technologies requireall BS's in a network to be very accurately time synchronized both fortransmitting MS voice communication as well as for other services suchas MS location. Accordingly, the MBS 148 will also require such timesynchronization. However, since an MBS 148 may not be in constantcommunication with the fixed location BS network (and indeed may beoff-line for substantial periods of time), on-board highly accuratetiming device may be necessary. In one embodiment, such a device may bea commercially available ribidium oscillator 1520 as shown in FIG. 11.

Since the MBS 148, includes a scaled down version of a BS 122 (denoted1522 in FIG. 11), it is capable of performing most typical BS 122 tasks,albeit on a reduced scale. In particular, the base station portion ofthe MBS 148 can:

-   -   (a) raise/lower its pilot channel signal strength,    -   (b) be in a state of soft hand-off with an MS 140, and/or    -   (c) be the primary BS 122 for an MS 140, and consequently be in        voice communication with the target MS (via the MBS operator        telephony interface 1524) if the MS supports voice        communication.        Further, the MBS 148 can, if it becomes the primary base station        communicating with the MS 140, request the MS to raise/lower its        power or, more generally, control the communication with the MS        (via the base station components 1522). However, since the MBS        148 will likely have substantially reduced telephony traffic        capacity in comparison to a standard infrastructure base station        122, note that the pilot channel for the MBS is preferably a        nonstandard pilot channel in that it should not be identified as        a conventional telephony traffic bearing BS 122 by MS's seeking        normal telephony communication. Thus, a target MS 140 requesting        to be located may, depending on its capabilities, either        automatically configure itself to scan for certain predetermined        MBS pilot channels, or be instructed via the fixed location base        station network (equivalently BS infrastructure) to scan for a        certain predetermined MBS pilot channel.

Moreover, the MBS 148 has an additional advantage in that it cansubstantially increase the reliability of communication with a target MS140 in comparison to the base station infrastructure by being able tomove toward or track the target MS 140 even if this MS is in (or movesinto) a reduced infrastructure base station network coverage area.Furthermore, an MBS 148 may preferably use a directional or smartantenna 1526 to more accurately locate a direction of signals from atarget MS 140. Thus, the sweeping of such a smart antenna 1526(physically or electronically) provides directional informationregarding signals received from the target MS 140. That is, suchdirectional information is determined by the signal propagation delay ofsignals from the target MS 140 to the angular sectors of one of moredirectional antennas 1526 on-board the MBS 148.

Before proceeding to further details of the MBS location subsystem 1508,an example of the operation of an MBS 148 in the context of respondingto a 911 emergency call is given. In particular, this example describesthe high level computational states through which the MBS 148transitions, these states also being illustrated in the state transitiondiagram of FIG. 12. Note that this figure illustrates the primary statetransitions between these MBS 148 states, wherein the solid statetransitions are indicative of a typical “ideal” progression whenlocating or tracking a target MS 140, and the dashed state transitionsare the primary state reversions due, for example, to difficulties inlocating the target MS 140.

Accordingly, initially the MBS 148 may be in an inactive state 1700,wherein the MBS location subsystem 1508 is effectively available forvoice or data communication with the fixed location base stationnetwork, but the MS 140 locating capabilities of the MBS are not active.From the inactive state 1700 the MBS (e.g., a police or rescue vehicle)may enter an active state 1704 once an MBS operator has logged onto theMBS location subsystem of the MBS, such logging being forauthentication, verification and journaling of MBS 148 events. In theactive state 1704, the MBS may be listed by a 911 emergency centerand/or the location center 142 as eligible for service in responding toa 911 request. From this state, the MBS 148 may transition to a readystate 1708 signifying that the MBS is ready for use in locating and/orintercepting a target MS 140. That is, the MBS 148 may transition to theready state 1708 by performing the following steps:

-   -   (1a) Synchronizing the timing of the location subsystem 1508        with that of the base station network infrastructure. In one        embodiment, when requesting such time synchronization from the        base station infrastructure, the MBS 148 will be at a        predetermined or well known location so that the MBS time        synchronization may adjust for a known amount of signal        propagation delay in the synchronization signal.    -   (1b) Establishing the location of the MBS 148. In one        embodiment, this may be accomplished by, for example, an MBS        operator identifying the predetermined or well known location at        which the MBS 148 is located.    -   (1c) Communicating with, for example, the 911 emergency center        via the fixed location base station infrastructure to identify        the MBS 148 as in the ready state.

Thus, while in the ready state 1708, as the MBS 148 moves, it has itslocation repeatedly (re)-estimated via, for example, GPS signals,location center 142 location estimates from the base stations 122 (and152), and an on-board deadreckoning subsystem 1527 having an MBSlocation estimator according to the programs described hereinbelow.However, note that the accuracy of the base station time synchronization(via the ribidium oscillator 1520) and the accuracy of the MBS 148location may need to both be periodically recalibrated according to (1a)and (1b) above.

Assuming a 911 signal is transmitted by a target MS 140, this signal istransmitted, via the fixed location base station infrastructure, to the911 emergency center and the location center 142, and assuming the MBS148 is in the ready state 1708, if a corresponding 911 emergency requestis transmitted to the MBS (via the base station infrastructure) from the911 emergency center or the location center, then the MBS may transitionto a seek state 1712 by performing the following steps:

-   -   (2a) Communicating with, for example, the 911 emergency response        center via the fixed location base station network to receive        the PN code for the target MS to be located (wherein this        communication is performed using the MS-like transceiver 1512        and/or the MBS operator telephony interface 1524).    -   (2b) Obtaining a most recent target MS location estimate from        either the 911 emergency center or the location center 142.    -   (2c) Inputting by the MBS operator an acknowledgment of the        target MS to be located, and transmitting this acknowledgment to        the 911 emergency response center via the transceiver 1512.

Subsequently, when the MBS 148 is in the seek state 1712, the MBS maycommence toward the target MS location estimate provided. Note that itis likely that the MBS is not initially in direct signal contact withthe target MS. Accordingly, in the seek state 1712 the following stepsmay be, for example, performed:

-   -   (3a) The location center 142 or the 911 emergency response        center may inform the target MS, via the fixed location base        station network, to lower its threshold for soft hand-off and at        least periodically boost its location signal strength.        Additionally, the target MS may be informed to scan for the        pilot channel of the MBS 148. (Note the actions here are not,        actions performed by the MBS 148 in the “seek state”; however,        these actions are given here for clarity and completeness.)    -   (3b) Repeatedly, as sufficient new MS location information is        available, the location center 142 provides new MS location        estimates to the MBS 148 via the fixed location base station        network.    -   (3c) The MBS repeatedly provides the MBS operator with new        target MS location estimates provided substantially by the        location center via the fixed location base station network.    -   (3d) The MBS 148 repeatedly attempts to detect a signal from the        target MS using the PN code for the target MS.    -   (3e) The MBS 148 repeatedly estimates its own location (as in        other states as well), and receives MBS location estimates from        the location center.

Assuming that the MBS 148 and target MS 140 detect one another (whichtypically occurs when the two units are within 0.25 to 3 miles of oneanother), the MBS enters a contact state 1716 when the target MS 140enters a soft hand-off state with the MBS. Accordingly, in the contactstate 1716, the following steps are, for example, performed:

-   -   (4a) The MBS 148 repeatedly estimates its own location.    -   (4b) Repeatedly, the location center 142 provides new target MS        140 and MBS location estimates to the MBS 148 via the fixed        location base station infrastructure network.    -   (4c) Since the MBS 148 is at least in soft hand-off with the        target MS 140, the MBS can estimate the direction and distance        of the target MS itself using, for example, detected target MS        signal strength and TOA as well as using any recent location        center target MS location estimates.    -   (4d) The MBS 148 repeatedly provides the MBS operator with new        target MS location estimates provided using MS location        estimates provided by the MBS itself and by the location center        via the fixed location base station network.

When the target MS 140 detects that the MBS pilot channel issufficiently strong, the target MS may switch to using the MBS 148 asits primary base station. When this occurs, the MBS enters a controlstate 1720, wherein the following steps are, for example, performed:

-   -   (5a) The MBS 148 repeatedly estimates its own location.    -   (5b) Repeatedly, the location center 142 provides new target MS        and MBS location estimates to the MBS 148 via the network of        base stations 122 (152).    -   (5c) The MBS 148 estimates the direction and distance of the        target MS 140 itself using, for example, detected target MS        signal strength and TOA as well as using any recent location        center target MS location estimates.    -   (5d) The MBS 148 repeatedly provides the MBS operator with new        target MS location estimates provided using MS location        estimates provided by the MBS itself and by the location center        142 via the fixed location base station network.    -   (5e) The MBS 148 becomes the primary base station for the target        MS 140 and therefore controls at least the signal strength        output by the target MS.

Note, there can be more than one MBS 148 tracking or locating an MS 140.There can also be more than one target MS 140 to be tracked concurrentlyand each target MS being tracked may be stationary or moving.

MBS Subsystem Architecture

An MBS 148 uses MS signal characteristic data for locating the MS 140.The MBS 148 may use such signal characteristic data to facilitatedetermining whether a given signal from the MS is a “direct shot” or anmultipath signal. That is, in one embodiment, the MBS 148 attempts todetermine or detect whether an MS signal transmission is receiveddirectly, or whether the transmission has been reflected or deflected.For example, the MBS may determine whether the expected signal strength,and TOA agree in distance estimates for the MS signal transmissions.Note, other signal characteristics may also be used, if there aresufficient electronics and processing available to the MBS 148; i.e.,determining signal phase and/or polarity as other indications ofreceiving a “direct shot” from an MS 140.

In one embodiment, the MBS 148 (FIG. 11) includes an MBS controller 1533for controlling the location capabilities of the MBS 148. In particular,the MBS controller 1533 initiates and controls the MBS state changes asdescribed in FIG. 12 above. Additionally, the MBS controller 1533 alsocommunicates with the location controller 1535, wherein this lattercontroller controls MBS activities related to MBS location and target MSlocation; e.g., this performs the program,“mobile_base_station_controller” described in APPENDIX A hereinbelow.The location controller 1535 receives data input from an event generator1537 for generating event records to be provided to the locationcontroller 1535. For example, records may be generated from data inputreceived from: (a) the vehicle movement detector 1539 indicating thatthe MBS 148 has moved at least a predetermined amount and/or has changeddirection by at least a predetermined angle, or (b) the MBS signalprocessing subsystem 1541 indicating that the additional signalmeasurement data has been received from either the location center 142or the target MS 140. Note that the MBS signal processing subsystem1541, in one embodiment, is similar to the signal processing subsystem1220 of the location center 142. Moreover, also note that there may bemultiple command schedulers. In particular, a scheduler 1528 forcommands related to communicating with the location center 142, ascheduler 1530 for commands related to GPS communication (via GPSreceiver 1531), a scheduler 1529 for commands related to the frequencyand granularity of the reporting of MBS changes in direction and/orposition via the MBS deadreckoning subsystem 1527 (note that thisscheduler is potentially optional and that such commands may be provideddirectly to the deadreckoning estimator 1544), and a scheduler 1532 forcommunicating with the target MS(s) 140 being located. Further, it isassumed that there is sufficient hardware and/or software to performcommands in different schedulers substantially concurrently.

In order to display an MBS computed location of a target MS 140, alocation of the MBS must be known or determined. Accordingly, each MBS148 has a plurality of MBS location estimators (or hereinafter alsosimply referred to as location estimators) for determining the locationof the MBS. Each such location estimator computes MBS locationinformation such as MBS location estimates, changes to MBS locationestimates, or, an MBS location estimator may be an interface forbuffering and/or translating a previously computed MBS location estimateinto an appropriate format. In particular, the MBS location module 1536,which determines the location of the MBS, may include the following MBSlocation estimators 1540 (also denoted baseline location estimators):

-   -   (a) a GPS location estimator 1540 a (not individually shown) for        computing an MBS location estimate using GPS signals,    -   (b) a location center location estimator 1540 b (not        individually shown) for buffering and/or translating an MBS        estimate received from the location center 142,    -   (c) an MBS operator location estimator 1540 c (not individually        shown) for buffering and/or translating manual MBS location        entries received from an MBS location operator, and    -   (d) in some MBS embodiments, an LBS location estimator 1540 d        (not individually shown) for the activating and deactivating of        LBS's 152. Note that, in high multipath areas and/or stationary        base station marginal coverage areas, such low cost location        base stations 152 (LBS) may be provided whose locations are        fixed and accurately predetermined and whose signals are        substantially only receivable within a relatively small range        (e.g., 2000 feet), the range potentially being variable. Thus,        by communicating with the LBS's 152 directly, the MBS 148 may be        able to quickly use the location information relating to the        location base stations for determining its location by using        signal characteristics obtained from the LBSs 152.        Note that each of the MBS baseline location estimators 1540,        such as those above, provide an actual MBS location rather than,        for example, a change in an MBS location. Further note that it        is an aspect of the present disclosure that additional MBS        baseline location estimators 1540 may be easily integrated into        the MBS location subsystem 1508 as such baseline location        estimators become available. For example, a baseline location        estimator that receives MBS location estimates from reflective        codes provided, for example, on streets or street signs can be        straightforwardly incorporated into the MBS location subsystem        1508.

Additionally, note that a plurality of MBS location technologies andtheir corresponding MBS location estimators are utilized due to the factthat there is currently no single location technology available that isboth sufficiently fast, accurate and accessible in substantially allterrains to meet the location needs of an MBS 148. For example, in manyterrains GPS technologies may be sufficiently accurate; however, GPStechnologies: (a) may require a relatively long time to provide aninitial location estimate (e.g., greater than 2 minutes); (b) when GPScommunication is disturbed, it may require an equally long time toprovide a new location estimate; (c) clouds, buildings and/or mountainscan prevent location estimates from being obtained; (d) in some casessignal reflections can substantially skew a location estimate. Asanother example, an MBS 148 may be able to use triangulation ortrilateralization technologies to obtain a location estimate; however,this assumes that there is sufficient (fixed location) infrastructure BScoverage in the area the MBS is located. Further, it is well known thatthe multipath phenomenon can substantially distort such locationestimates. Thus, for an MBS 148 to be highly effective in variedterrains, an MBS is provided with a plurality of location technologies,each supplying an MBS location estimate.

In fact, much of the architecture of the location engine 139 could beincorporated into an MBS 148. For example, in some embodiments of theMBS 148, the following FOMs 1224 may have similar location modelsincorporated into the MBS:

-   -   (a) a variation of the distance FOM 1224 wherein TOA signals        from communicating fixed location BS's are received (via the MBS        transceiver 1512) by the MBS and used for providing a location        estimate;    -   (b) a variation of the artificial neural net based FOMs 1224 (or        more generally a location learning or a classification model)        may be used to provide MBS location estimates via, for example,        learned associations between fixed location BS signal        characteristics and geographic locations;    -   (c) an LBS location FOM 1224 for providing an MBS with the        ability to activate and deactivate LBS's to provide (positive)        MBS location estimates as well as negative MBS location regions        (i.e., regions where the MBS is unlikely to be since one or more        LBS's are not detected by the MBS transceiver);    -   (d) one or more MBS location reasoning agents and/or a location        estimate heuristic agents for resolving MBS location estimate        conflicts and providing greater MBS location estimate accuracy.        For example, modules similar to the analytical reasoner module        1416 and the historical location reasoner module 1424.

However, for those MBS location models requiring communication with thebase station infrastructure, an alternative embodiment is to rely on thelocation center 142 to perform the computations for at least some ofthese MBS FOM models. That is, since each of the MBS location modelsmentioned immediately above require communication with the network offixed location BS's 122 (152), it may be advantageous to transmit MBSlocation estimating data to the location center 142 as if the MBS wereanother MS 140 for the location center to locate, and thereby rely onthe location estimation capabilities at the location center rather thanduplicate such models in the MBS 148. The advantages of this approachare that:

-   -   (a) an MBS is likely to be able to use less expensive processing        power and software than that of the location center;    -   (b) an MBS is likely to require substantially less memory,        particularly for data bases, than that of the location center.

As will be discussed further below, in one embodiment of the MBS 148,there are confidence values assigned to the locations output by thevarious location estimators 1540. Thus, the confidence for a manualentry of location data by an MBS operator may be rated the highest andfollowed by the confidence for (any) GPS location data, followed by theconfidence for (any) location center location 142 estimates, followed bythe confidence for (any) location estimates using signal characteristicdata from LBSs. However, such prioritization may vary depending on, forinstance, the radio coverage area 120. In one embodiment of the presentdisclosure, for MBS location data received from GPS, and the locationcenter, their confidences may vary according to the area in which theMBS 148 resides. That is, if it is known that for a given area, there isa reasonable probability that a GPS signal may suffer multipathdistortions and that the location center has in the past providedreliable location estimates, then the confidences for these two locationsources may be reversed.

In one embodiment of the present disclosure, MBS operators may berequested to occasionally manually enter the location of the MBS 148when the MBS is stationary for determining and/or calibrating theaccuracy of various MBS location estimators.

There is an additional important source of location information for theMBS 148 that is incorporated into an MBS vehicle (such as a policevehicle) that has no comparable functionality in the network of fixedlocation BS's. That is, the MBS 148 may use deadreckoning informationprovided by a deadreckoning MBS location estimator 1544 whereby the MBSmay obtain MBS deadreckoning location change estimates. Accordingly, thedeadreckoning MBS location estimator 1544 may use, for example, anon-board gyroscope 1550, a wheel rotation measurement device (e.g.,odometer) 1554, and optionally an accelerometer (not shown). Thus, sucha deadreckoning MBS location estimator 1544 periodically provides atleast MBS distance and directional data related to MBS movements from amost recent MBS location estimate. More precisely, in the absence of anyother new MBS location information, the deadreckoning MBS locationestimator 1544 outputs a series of measurements, wherein each suchmeasurement is an estimated change (or delta) in the position of the MBS148 between a request input timestamp and a closest time prior to thetimestamp, wherein a previous deadreckoning terminated. Thus, eachdeadreckoning location change estimate includes the following fields:

-   -   (a) an “earliest timestamp” field for designating the start time        when the deadreckoning location change estimate commences        measuring a change in the location of the MBS;    -   (b) a “latest timestamp” field for designating the end time when        the deadreckoning location change estimate stops measuring a        change in the location of the MBS; and    -   (c) an MBS location change vector.        That is, the “latest timestamp” is the timestamp input with a        request for deadreckoning location data, and the “earliest        timestamp” is the timestamp of the closest time, T, prior to the        latest timestamp, wherein a previous deadreckoning output has        its a timestamp at a time equal to T.

Further, the frequency of such measurements provided by thedeadreckoning subsystem 1527 may be adaptively provided depending on thevelocity of the MBS 148 and/or the elapsed time since the most recentMBS location update. Accordingly, the architecture of at least someembodiments of the MBS location subsystem 1508 must be such that it canutilize such deadreckoning information for estimating the location ofthe MBS 148.

In one embodiment of the MBS location subsystem 1508 described infurther detail hereinbelow, the outputs from the deadreckoning MBSlocation estimator 1544 are used to synchronize MBS location estimatesfrom different MBS baseline location estimators. That is, since such adeadreckoning output may be requested for substantially any time fromthe deadreckoning MBS location estimator, such an output can berequested for substantially the same point in time as the occurrence ofthe signals from which a new MBS baseline location estimate is derived.Accordingly, such a deadreckoning output can be used to update other MBSlocation estimates not using the new MBS baseline location estimate.

It is assumed that the error with deadreckoning increases withdeadreckoning distance. Accordingly, it is an aspect of the embodimentof the MBS location subsystem 1508 that when incrementally updating thelocation of the MBS 148 using deadreckoning and applying deadreckoninglocation change estimates to a “most likely area” in which the MBS 148is believed to be, this area is incrementally enlarged as well asshifted. The enlargement of the area is used to account for theinaccuracy in the deadreckoning capability. Note, however, that thedeadreckoning MBS location estimator is periodically reset so that theerror accumulation in its outputs can be decreased. In particular, suchresetting occurs when there is a high probability that the location ofthe MBS is known. For example, the deadreckoning MBS location estimatormay be reset when an MBS operator manually enters an MBS location orverifies an MBS location, or a computed MBS location has sufficientlyhigh confidence.

Thus, due to the MBS 148 having less accurate location information (bothabout itself and a target MS 140), and further that deadreckoninginformation must be utilized in maintaining MBS location estimates, afirst embodiment of the MBS location subsystem architecture is somewhatdifferent from the location engine 139 architecture. That is, thearchitecture of this first embodiment is simpler than that of thearchitecture of the location engine 139. However, it important to notethat, at a high level, the architecture of the location engine 139 mayalso be applied for providing a second embodiment of the MBS locationsubsystem 1508, as one skilled in the art will appreciate afterreflecting on the architectures and processing provided at an MBS 148.For example, an MBS location subsystem 1508 architecture may be providedthat has one or more first order models 1224 whose output is suppliedto, for example, a blackboard or expert system for resolving MBSlocation estimate conflicts, such an architecture being analogous to oneembodiment of the location engine 139 architecture.

Furthermore, it is also an important aspect of the present disclosurethat, at a high level, the MBS location subsystem architecture may alsobe applied as an alternative architecture for the location engine 139.For example, in one embodiment of the location engine 139, each of thefirst order models 1224 may provide its MS location hypothesis outputsto a corresponding “location track,” analogous to the MBS locationtracks described hereinbelow, and subsequently, a most likely MS currentlocation estimate may be developed in a “current location track” (alsodescribed hereinbelow) using the most recent location estimates in otherlocation tracks.

Further, note that the ideas and methods discussed here relating to MBSlocation estimators 1540 and MBS location tracks, and, the relatedprograms hereinbelow are sufficiently general so that these ideas andmethods may be applied in a number of contexts related to determiningthe location of a device capable of movement and wherein the location ofthe device must be maintained in real time. For example, the presentideas and methods may be used by a robot in a very cluttered environment(e.g., a warehouse), wherein the robot has access: (a) to a plurality of“robot location estimators” that may provide the robot with sporadiclocation information, and (b) to a deadreckoning location estimator.

Each MBS 148, additionally, has a location display (denoted the MBSoperator visual user interface 1558 in FIG. 11) where area maps that maybe displayed together with location data. In particular, MS locationdata may be displayed on this display as a nested collection of areas,each smaller nested area being the most likely area within (any)encompassing area for locating a target MS 140. Note that the MBScontroller algorithm below may be adapted to receive location center 142data for displaying the locations of other MBSs 148 as well as targetMSs 140.

Further, the MBS 148 may constrain any location estimates to streets ona street map using the MBS location snap to street module 1562. Forexample, an estimated MBS location not on a street may be “snapped to” anearest street location. Note that a nearest street location determinermay use “normal” orientations of vehicles on streets as a constraint onthe nearest street location, particularly, if an MBS 148 is moving attypical rates of speed and acceleration, and without abrupt changes indirection. For example, if the deadreckoning MBS location estimator 1544indicates that the MBS 148 is moving in a northerly direction, then thestreet snapped to should be a north-south running street. Moreover, theMBS location snap to street module 1562 may also be used to enhancetarget MS location estimates when, for example, it is known or suspectedthat the target MS 140 is in a vehicle and the vehicle is moving attypical rates of speed. Furthermore, the snap to street location module1562 may also be used in enhancing the location of a target MS 140 byeither the MBS 148 or by the location engine 139. In particular, thelocation estimator 1344 or an additional module between the locationestimator 1344 and the output gateway 1356 may utilize an embodiment ofthe snap to street location module 1562 to enhance the accuracy oftarget MS 140 location estimates that are known to be in vehicles. Notethat this may be especially useful in locating stolen vehicles that haveembedded wireless location transceivers (MSs 140), wherein appropriatewireless signal measurements can be provided to the location center 142.

MBS Data Structure Remarks

Assuming the existence of at least some of the location estimators 1540that were mentioned above, the discussion here refers substantially tothe data structures and their organization as illustrated in FIG. 13.

The location estimates (or hypotheses) for an MBS 148 determining itsown location each have an error or range estimate associated with theMBS location estimate. That is, each such MBS location estimate includesa “most likely MBS point location” within a “most likely area”. The“most likely MBS point location” is assumed herein to be the centroid ofthe “most likely area.” In one embodiment of the MBS location subsystem1508, a nested series of “most likely areas” may be provided about amost likely MBS point location. However, to simplify the discussionherein each MBS location estimate is assumed to have a single “mostlikely area”. One skilled in the art will understand how to provide suchnested “most likely areas” from the description herein. Additionally, itis assumed that such “most likely areas” are not grossly oblong; i.e.,area cross sectioning lines through the centroid of the area do not havelarge differences in their lengths. For example, for any such “mostlikely area”, A, no two such cross sectioning lines of A may havelengths that vary by more than a factor of two.

Each MBS location estimate also has a confidence associated therewithproviding a measurement of the perceived accuracy of the MBS being inthe “most likely area” of the location estimate.

A (MBS) “location track” is a data structure (or object) having a queueof a predetermined length for maintaining a temporal (timestamp)ordering of “location track entries” such as the location track entries1770 a, 1770 b, 1774 a, 1774 b, 1778 a, 1778 b, 1782 a, 1782 b, and 1786a (FIG. 13), wherein each such MBS location track entry is an estimateof the location of the MBS at a particular corresponding time.

There is an MBS location track for storing MBS location entries obtainedfrom MBS location estimation information from each of the MBS baselinelocation estimators described above (i.e., a GPS location track 1750 forstoring MBS location estimations obtained from the GPS locationestimator 1540, a location center location track 1754 for storing MBSlocation estimations obtained from the location estimator 1540 derivingits MBS location estimates from the location center 142, an LBS locationtrack 1758 for storing MBS location estimations obtained from thelocation estimator 1540 deriving its MBS location estimates from basestations 122 and/or 152, and a manual location track 1762 for MBSoperator entered MBS locations). Additionally, there is one furtherlocation track, denoted the “current location track” 1766 whose locationtrack entries may be derived from the entries in the other locationtracks (described further hereinbelow). Further, for each locationtrack, there is a location track head that is the head of the queue forthe location track. The location track head is the most recent (andpresumably the most accurate) MBS location estimate residing in thelocation track. Thus, the GPS location track 1750 has location trackhead 1770; the location center location track 1754 has location trackhead 1774; the LBS location track 1758 has location track head 1778; themanual location track 1762 has location track head 1782; and the currentlocation track 1766 has location track head 1786. Additionally, fornotational convenience, for each location track, the time series ofprevious MBS location estimations (i.e., location track entries) in thelocation track will herein be denoted the “path for the location track.”Such paths are typically the length of the location track queuecontaining the path. Note that the length of each such queue may bedetermined using at least the following considerations:

-   -   (i) In certain circumstances (described hereinbelow), the        location track entries are removed from the head of the location        track queues so that location adjustments may be made. In such a        case, it may be advantageous for the length of such queues to be        greater than the number of entries that are expected to be        removed;    -   (ii) In determining an MBS location estimate, it may be        desirable in some embodiments to provide new location estimates        based on paths associated with previous MBS location estimates        provided in the corresponding location track queue.        Also note that it is within the scope of the present disclosure        that the location track queue lengths may be a length of one.

Regarding location track entries, each location track entry includes:

-   -   (a) a “derived location estimate” for the MBS that is derived        using at least one of:        -   (i) at least a most recent previous output from an MBS            baseline location estimator 1540 (i.e., the output being an            MBS location estimate);        -   (ii) deadreckoning output information from the deadreckoning            subsystem 1527. Further note that each output from an MBS            location estimator has a “type” field that is used for            identifying the MBS location estimator of the output.    -   (b) an “earliest timestamp” providing the time/date when the        earliest MBS location information upon which the derived        location estimate for the MBS depends. Note this will typically        be the timestamp of the earliest MBS location estimate (from an        MBS baseline location estimator) that supplied MBS location        information used in deriving the derived location estimate for        the MBS 148.    -   (c) a “latest timestamp” providing the time/date when the latest        MBS location information upon which the derived location        estimate for the MBS depends. Note that earliest        timestamp=latest timestamp only for so called “baseline entries”        as defined hereinbelow. Further note that this attribute is the        one used for maintaining the “temporal (timestamp) ordering” of        location track entries.    -   (d) A “deadreckoning distance” indicating the total distance        (e.g., wheel turns or odometer difference) since the most        recently previous baseline entry for the corresponding MBS        location estimator for the location track to which the location        track entry is assigned.

For each MBS location track, there are two categories of MBS locationtrack entries that may be inserted into a MBS location track:

-   -   (a) “baseline” entries, wherein each such baseline entry        includes (depending on the location track) a location estimate        for the MBS 148 derived from: (i) a most recent previous output        either from a corresponding MBS baseline location estimator,        or (ii) from the baseline entries of other location tracks (this        latter case being the for the “current” location track);    -   (b) “extrapolation” entries, wherein each such entry includes an        MBS location estimate that has been extrapolated from the (most        recent) location track head for the location track (i.e., based        on the track head whose “latest timestamp” immediately precedes        the latest timestamp of the extrapolation entry). Each such        extrapolation entry is computed by using data from a related        deadreckoning location change estimate output from the        deadreckoning MBS location estimator 1544. Each such        deadreckoning location change estimate includes measurements        related to changes or deltas in the location of the MBS 148.        More precisely, for each location track, each extrapolation        entry is determined using: (i) a baseline entry, and (ii) a set        of one or more (i.e., all later occurring) deadreckoning        location change estimates in increasing “latest timestamp”        order. Note that for notational convenience this set of one or        more deadreckoning location change estimates will be denoted the        “deadreckoning location change estimate set” associated with the        extrapolation entry resulting from this set.    -   (c) Note that for each location track head, it is either a        baseline entry or an extrapolation entry. Further, for each        extrapolation entry, there is a most recent baseline entry, B,        that is earlier than the extrapolation entry and it is this B        from which the extrapolation entry was extrapolated. This        earlier baseline entry, B, is hereinafter denoted the “baseline        entry associated with the extrapolation entry.” More generally,        for each location track entry, T, there is a most recent        previous baseline entry, B, associated with T, wherein if T is        an extrapolation entry, then B is as defined above, else if T is        a baseline entry itself, then T=B. Accordingly, note that for        each extrapolation entry that is the head of a location track,        there is a most recent baseline entry associated with the        extrapolation entry.

Further, there are two categories of location tracks:

-   -   (a) “baseline location tracks,” each having baseline entries        exclusively from a single predetermined MBS baseline location        estimator; and    -   (b) a “current” MBS location track having entries that are        computed or determined as “most likely” MBS location estimates        from entries in the other MBS location tracks.        MBS Location Estimating Strategy

In order to be able to properly compare the track heads to determine themost likely MBS location estimate it is an aspect of the presentdisclosure that the track heads of all location tracks include MBSlocation estimates that are for substantially the same (latest)timestamp. However, the MBS location information from each MBS baselinelocation estimator is inherently substantially unpredictable andunsynchronized. In fact, the only MBS location information that may beconsidered predicable and controllable is the deadreckoning locationchange estimates from the deadreckoning MBS location estimator 1544 inthat these estimates may reliably be obtained whenever there is a queryfrom the location controller 1535 for the most recent estimate in thechange of the location for the MBS 148. Consequently (referring to FIG.13), synchronization records 1790 (having at least a 1790 b portion, andin some cases also having a 1790 a portion) may be provided for updatingeach location track with a new MBS location estimate as a new trackhead. In particular, each synchronization record includes adeadreckoning location change estimate to be used in updating all but atmost one of the location track heads with a new MBS location estimate byusing a deadreckoning location change estimate in conjunction with eachMBS location estimate from an MBS baseline location estimator, thelocation track heads may be synchronized according to timestamp. Moreprecisely, for each MBS location estimate, E, from an MBS baselinelocation estimator, an embodiment of a novel wireless location systemand/or method disclosed herein also substantially simultaneously queriesthe deadreckoning MBS location estimator for a corresponding most recentchange in the location of the MBS 148. Accordingly, E and the retrievedMBS deadreckoning location change estimate, C, have substantially thesame “latest timestamp”. Thus, the location estimate E may be used tocreate a new baseline track head for the location track having thecorresponding type for E, and C may be used to create a correspondingextrapolation entry as the head of each of the other location tracks.Accordingly, since for each MBS location estimate, E, there is a MBSdeadreckoning location change estimate, C, having substantially the same“latest timestamp”, E and C will be hereinafter referred as “paired.”

High level descriptions of an embodiment of the location functionsperformed by an MBS 148 are provided in APPENDIX A hereinbelow.

The present disclosure has been presented for purposes of illustrationand description. Further, the description herein is not intended tolimit the present disclosure to the form disclosed herein. Consequently,variation and modification commiserate with the above teachings, withinthe skill and knowledge of the relevant art, are within the scope of thepresent disclosure. The present disclosure is further intended toexplain the best mode presently known of practicing the invention asrecited in the claims, and to enable others skilled in the art toutilize the present disclosure, or other embodiments derived therefrom,e.g., with the various modifications required by their particularapplication or uses of the present disclosure.

A Second Embodiment of the Context Adjuster

Note that in this second embodiment of the Context Adjuster, it usesvarious heuristics to increment/decrement the confidence value of thelocation hypotheses coming from the First Order Models. These heuristicsare implemented using fuzzy mathematics, wherein linguistic, fuzzy“if-then” rules embody the heuristics. That is, each fuzzy rule includesterms in both the “if” and the “then” portions that are substantiallydescribed using natural language—like terms to denote various parametervalue classifications related to, but not equivalent to, probabilitydensity functions. Further note that the Context Adjuster and/or theFOM's may be calibrated using the location information from LBSs (i.e.,fixed location BS transceivers), via the Location Base Station Modelsince such LBS's have well known and accurate predetermined locations.

Regarding the heuristics of the present embodiment of the contextadjuster, the following is an example of a fuzzy rule that might appearin this embodiment of the Context Adjuster:

If <the season is Fall> then <the confidence level of Distance Model isincreased by 5%>.

In the above sample rule, “Distance Model” denotes a First Order Modelutilized by the present invention. To apply this sample rule, the fuzzysystem needs a concrete definition of the term “Fall.” In traditionalexpert systems, the term Fall would be described by a particular set ofmonths, for example, September through November, in which traditionalset theory is applied. In traditional set theory, an entity, in thiscase a date, is either in a set or it is not in a set, e.g. its degreeof membership in a set is either 0, indicating that the entity is not ina particular set, or 1, indicating that the entity is in the set.However, the traditional set theory employed in expert systems does notlend itself well to entities that fall on set boundaries. For example, atraditional expert system could take dramatically different actions fora date of August 31 than it could for a date of September 1 becauseAugust 31 might belong to the set “Summer” while the date September 1might belong to the set “Fall.” This is not a desirable behavior sinceit is extremely difficult if not impossible to determine such lines ofdemarcation so accurately. However, fuzzy mathematics allows for thepossibility of an entity belonging to multiple sets with varying degreesof confidence ranging from a minimum value of 0 (indicating that theconfidence the entity belongs to the particular set is minimum) to 1(indicating that the confidence the entity belongs to the particular setis maximum). The “fuzzy boundaries” between the various sets aredescribed by fuzzy membership functions which provide a membershipfunction value for each value on the entire range of a variable. As aconsequence of allowing entities to belong to multiple setssimultaneously, the fuzzy rule base might have more than one rule thatis applicable for any situation. Thus, the actions prescribed by theindividual rules are averaged via a weighting scheme where each rule isimplemented in proportion to its minimum confidence. For furtherinformation regarding such fuzzy heuristics, the following referencesare incorporated herein by reference: (McNeil and Freiberger, 1993; Cox,1994; Klir and Folger, 1999; Zimmerman, 1991).

Thus, the rules defined in the fuzzy rule base in conjunction with themembership functions allow the heuristics for adjusting confidencevalues to be represented in a linguistic form more readily understood byhumans than many other heuristic representations and thereby making iteasier to maintain and modify the rules. The fuzzy rule base with itsmembership functions can be thought of as an extension to a traditionalexpert system. Thus, since traditional expert systems are subsets offuzzy systems, an alternative to a fuzzy rule base is a traditionalexpert system, and it is implicit that anywhere in the description ofthe current invention that a fuzzy rule base can be replaced with anexpert system.

Also, these heuristics may evolve over time by employing adaptivemechanisms including, but not limited to, genetic algorithms to adjustor tune various system values in accordance with past experiences andpast performance of the Context Adjuster for increasing the accuracy ofthe adjustments made to location hypothesis confidence values. Forexample, in the sample rule presented above:

If <the season is Fall> then <the confidence level of Distance Model isincreased by 5%> an adaptive mechanism or optimization routine can beused to adjust the percent increase in the confidence level of theDistance Model. For example, by accessing the MS Status Repository, agenetic algorithm is capable of adjusting the fuzzy rules and membershipfunctions such that the location hypotheses are consistent with amajority of the verified MS locations. In this way, the Context Adjusteris able to employ a genetic algorithm to improve its performance overtime. For further information regarding such adaptive mechanisms, thefollowing references are incorporated herein by reference: (Goldberg,1989; Holland, 1975). For further information regarding the tuning offuzzy systems using such adaptive mechanisms, the following referencesare incorporated herein by reference: (Karr, 1991a, 1991b).

In one embodiment, the Context Adjuster alters the confidence values oflocation hypotheses according to one or more of the followingenvironmental factors: (1) the type of region (e.g., dense urban, urban,rural, etc.), (2) the month of the year, (3) the time of day, and (4)the operational status of base stations (e.g., on-line or off-line), aswell as other environmental factors that may substantially impact theconfidence placed in a location hypothesis. Note that in thisembodiment, each environmental factor has an associated set oflinguistic heuristics and associated membership functions that prescribechanges to be made to the confidence values of the input locationhypotheses.

The context adjuster begins by receiving location hypotheses andassociated confidence levels from the First Order Models. The ContextAdjuster takes this information and improves and refines it based onenvironmental information using the modules described below.

B.1 COA Calculation Module

As mentioned above each location hypothesis provides an approximation tothe MS position in the form of a geometric shape and an associatedconfidence value, a. The COA calculation module determines a center ofarea (COA) for each of the geometric shapes, if such a COA is notalready provided in a location hypothesis. The COA Calculation Modulereceives the following information from each First Order Model: (1) ageometrical shape and (2) an associated confidence value, a. The COAcalculation is made using traditional geometric computations (numericalalgorithms are readily available). Thus, following this step, eachlocation hypothesis includes a COA as a single point that is assumed torepresent the most likely approximation of the location of the MS. TheCOA Calculation Module passes the following information to thefuzzification module: (1) a geometrical shape associated with each firstorder model 1224, (2) an associated confidence value, and (3) anassociated COA.

B.2 Fuzzification Module

A fuzzification module receives the following information from the COACalculation Module: (1) a geometrical shape associated with each FirstOrder Model, (2) an associated confidence value, and (3) an associatedCOA. The Fuzzification Module uses this information to compute amembership function value (μ) for each of the M location hypothesesreceived from the COA calculation module (where the individual modelsare identified with an i index) for each of the N environmental factors(identified with a j index). In addition to the information receivedfrom the COA Calculation Module, the Fuzzification Module receivesinformation from the Location Center Supervisor. The fuzzificationmodule uses current environmental information such as the current timeof day, month of year, and information about the base stations on-linefor communicating with the MS associated with a location hypothesiscurrently being processed (this information may include, but is notlimited to, the number of base stations of a given type, e.g., locationbase stations, and regular base stations, that have a previous historyof being detected in an area about the COA for a location hypothesis).The base station coverage information is used to compute a percentage ofbase stations reporting for each location hypothesis.

The fuzzification is achieved in the traditional fashion using fuzzymembership functions for each environmental factor as, for example, isdescribed in the following references incorporated herein by reference:(McNeil and Freiberger, 1993; Cox, 1994; Klir and Folger, 1999;Zimmerman, 1991).

Using the geographical area types for illustration purposes here, thefollowing procedure might be used in the Fuzzification Module. Eachvalue of COA for a location hypothesis is used to compute membershipfunction values (μ) for each of five types of areas: (1) dense urban(μ_(DU)), (2) urban (μ_(U)), (3) suburban (μ_(S)), (4) rural plain(μ_(RP)), and (5) rural mountains (μ_(RM)). These membership functionvalues provide the mechanism for representing degrees of membership inthe area types, these area types being determined from an area map thathas been sectioned off. In accordance with fuzzy theory, there may begeographical locations that include, for example, both dense urban andurban areas; dense urban and rural plane areas; dense urban, urban, andrural plane areas, etc. Thus for a particular MS location area estimate(described by a COA), it may be both dense urban and urban at the sametime. The resolution of any apparent conflict in applicable rules islater resolved in the Defuzzification Module using the fuzzy membershipfunction values (μ) computed in the Fuzzification Module.

Any particular value of a COA can land in more than one area type. Forexample, the COA may be in both dense urban and urban. Further, in somecases a location hypothesis for a particular First Order Model i mayhave membership functions μ_(DU) ^(i), μ_(U) ^(i), μ_(S) ^(i), μ_(RP)^(i), and μ_(RM) ^(i) wherein they all potentially have non-zero values.Additionally, each geographical area is contoured. Note that themembership function contours allow for one distinct value of membershipfunction to be determined for each COA location (i.e., there will bedistinct values of μ_(DU) ^(i), μ_(U) ^(i), μ_(S) ^(i), μ_(RP) ^(i), andμ_(RM) ^(i) for any single COA value associated with a particular modeli). For example, the COA would have a dense urban membership functionvalue, μ_(DU) ^(i), equal to 0.5. Similar contours would be used tocompute values of μ_(U) ^(i), μ_(S) ^(i), μ_(RP) ^(i), and μ_(RM) ^(i).

Thus, for each COA, there now exists an array or series of membershipfunction values; there are K membership function values (K=number ofdescriptive terms for the specified environmental factor) for each of MFirst Order Models. Each COA calculation has associated with it adefinitive value for μ_(DU) ^(i), μ_(U) ^(i), μ_(S) ^(i), μ_(RP) ^(i),and μ_(RM) ^(i). Taken collectively, the M location hypotheses withmembership function values for the K descriptive terms for theparticular environmental factor results in a membership function valuematrix. Additionally, similar membership function values are computedfor each of the N environmental factors, thereby resulting in acorresponding membership function value matrix for each of the Nenvironmental factors.

The Fuzzification Module passes the N membership function value matricesdescribed above to the Rule Base Module along with all of theinformation it originally received from the COA Calculation Module.

B.3 Rule Base Module

The Rule Base Module receives from the Fuzzification Module thefollowing information: (1) a geometrical shape associated with eachFirst Order Model, (2) an associated confidence value, (3) an associatedCOA, and (4) N membership function value matrices. The Rule Base Moduleuses this information in a manner consistent with typical fuzzy rulebases to determine a set of active or applicable rules. Sample ruleswere provided in the general discussion of the Context Adjuster.Additionally, references have been supplied that describe the necessarycomputations. Suffice it to say that the Rule Base Modules employ theinformation provided by the Fuzzification Module to compute confidencevalue adjustments for each of the m location hypotheses. Associated witheach confidence value adjustment is a minimum membership function valuecontained in the membership function matrices computed in theFuzzification Module.

For each location hypothesis, a simple inference engine driving the rulebase queries the performance database (FIG. 8(3)) to determine how wellthe location hypotheses for the First Order Model providing the currentlocation hypothesis has performed in the past (for a geographic areasurrounding the MS location estimate of the current location hypothesis)under the present environmental conditions. For example, the performancedatabase is consulted to determine how well this particular First OrderModel has performed in the past in locating an MS for the given time ofday, month of year, and area type. Note that the performance value is avalue between 0 and 1 wherein a value of 0 indicates that the model is apoor performer, while a value of 1 indicates that the model is always(or substantially always) accurate in determining an MS location underthe conditions (and in the area) being considered. These performancevalues are used to compute values that are attached to the currentconfidence of the current location hypothesis; i.e., these performancevalues serve as the “then” sides of the fuzzy rules; the First OrderModels that have been effective in the past have their confidence levelsincremented by large amounts while First Order Models that have beenineffective in the past have their confidence levels incremented bysmall amounts. This information is received from the PerformanceDatabase in the form of an environmental factor, a First Order Modelnumber, and a performance value. Accordingly, an intermediate value forthe adjustment of the confidence value for the current locationhypothesis is computed for each environmental condition (used by ContextAdjuster) based on the performance value retrieved from the PerformanceDatabase. Each of these intermediate adjustment values are computedaccording to the following equation which is applicable to areainformation:adjustment_(j) ^(i) =Δa _(j) ^(i)=performance_value_(j)*Δa_(REGION)^(MAX)where a is the confidence value of a particular location hypothesis,performance_value is the value obtained from the Performance Database,Δa_(REGION) ^(MAX) is a system parameter that accounts for how importantthe information is being considered by the context adjuster.Furthermore, this parameter is initially provided by an operator in, forexample, a system start-up configuration and a reasonable value for thisparameter is believed to be in the range 0.05 to 0.1, the subscript jrepresents a particular environmental factor, and the superscript irepresents a particular First Order Model. However, it is an importantaspect of the present invention that this value can be repeatedlyaltered by an adaptive mechanism such as a genetic algorithm forimproving the MS location accuracy of the present invention. In thisway, and because the rules are “written” using current performanceinformation as stored in the Performance Database, the Rule Module isdynamic and becomes more accurate with time.

The Rule Base Module passes the matrix of adjustments to theDefuzzification Module along with the membership function value matricesreceived from the Fuzzification Module.

B.6 Defuzzification Module

The Defuzzification Module receives the matrix of adjustments and themembership function value matrices from the Rule Base Module. The finaladjustment to the First Order Model confidence values as computed by theContext Adjuster is computed according to:

such as, but not limited to, time of day, month of year, and basestation coverage, there are a number of system start-up

${{\Delta\alpha}_{j}^{i}(k)} = \frac{\sum\limits_{j = 1}^{N}\;{{\mu_{j}^{i}(k)}{\Delta\alpha}_{j}^{i}}}{\sum\limits_{j = 1}^{N}\;{\mu_{j}^{i}(k)}}$configuration parameters that can be adjusted in attempts to improvesystem performance. These adjustments are, in effect, adjustmentscomputed depending on the previous performance values of each modelunder similar conditions as being currently considered. Theseadjustments are summed and forwarded to the blackboard. Thus, theContext Adjuster passes the following information to the blackboard:adjustments in confidence values for each of the First Order Modelsbased on environmental factors and COA values associated with eachlocation hypothesis.Summary

The Context Adjuster uses environmental factor information and pastperformance information for each of i First Order Models to computeadjustments to the current confidence values. It retrieves informationfrom the First Order Models, interacts with the Supervisor and thePerformance Database, and computes adjustments to the confidence values.Further, the Context Adjuster employs a genetic algorithm to improve theaccuracy of its calculations. The algorithm for the Context Adjuster isincluded in algorithm BE.B below:

Algorithm BE.B: Pseudocode for the Context Adjuster.

Context_Adjuster (geometries, alpha) /* This program implements theContext Adjuster. It receives from the First Order Models geometricareas contained in a data structure called geometries, and associatedconfidence values contained in an array called alpha. The program usedenvironmental information to compute improved numerical values of theconfidence values. It places the improved values in the array calledalpha, destroying the previous values in the process. */ // pseudo codefor the Context Adjuster // assume input from each of i models includesa // geographical area described by a number of points // and aconfidence value alpha(i). alpha is such // that if it is 0.0 then themodel is absolutely // sure that the MS is not in the prescribed area;// if it is 1.0 then the model is absolutely // sure that the MS is inthe prescribed area. // calculate the center of area for each of the imodel areas for i = 1 to number_of_models  calculate center of area //termed coa(i) from here on out // extract information from the “outsideworld” or the environment find time_of_day find month_of_year findnumber_of_BS_available find number_of_BS_reporting // calculatepercent_coverage of base stations percent_coverage = 100.0*(number_of_BS_reporting / number_of_BS_available) // use these j = 4environmental factors to compute adjustments to the // i confidencevalues associated with the i models - alpha(i) for i = 1 tonumber_of_models // loop on the number of models  for j = 1 tonumber_env_factors // loop on the number of  environmental factors   fork = 1 to number_of_fuzzy_classes // loop   on the number of classes            // used for each of the environmental             // factors   // calculate mu values based on membership function definitions   calculate mu(i,j,k) values    // go to the performance database andextract current performance      // information for each of the imodels, in the k fuzzy classes,      for the j environmental factors   fetch performance(i,j,k)    // calculate the actual values for theright hand sides of the fuzzy    rules    delta_alpha(i,j,k) =performance(i,j,k) * delta_alpha_max(j)    // delta_alpha_max(j) is amaximum amount each environmental    // factor can alter the confidencevalue; it is eventually    // determined by a genetic algorithm    //compute a weighted average; this is traditional fuzzy    mathematics   delta_alpha(i,j,k) = sum[mu(i,j,k) * delta_alpha(i,j,k) /   sum[mu(i,j,k)]    end loop on k // number of fuzzy classes   //compute final delta_alpha values   delta_alpha(i) =sum[delta_alpha(i,j)]   end loop on j  // number of environmentalfactors  alpha(i) += delta_alpha(i)  end loop on i  // number of models// send alpha values to blackboard send delta_alpha(i) to blackboard //see if it is time to interact with a genetic algorithm if (in_progress) then continue to calculate alpha adjustments else  call the geneticalgorithm to adjust alpha_max parameters and mu  functions

APPENDIX E Historical Data Confidence Adjuster ProgramHistorical_data_confidence_adjuster(loc_hyp) /* This function adjuststhe confidence of location hypothesis, “loc_hyp”, according to how wellits location signature cluster  fits with verified location signatureclusters in the location signature data base. */ {     mesh ←get_mesh_for(loc_hyp.FOM_ID); // each FOM has a mesh of the LocationCenter service area     covering ←get_mesh_covering_of_MS_estimate_for(loc_hyp); /* get the cells of“mesh” that minimally cover              the most pertinent target MSestimate in “loc_hyp”. */     total_per_unit_error ← 0; //initialization     for each cell, C, of “covering” do /* determine anerror measurement between the location signature cluster of                   “loc_hyp” and the verified location signatureclusters in the cell */     {         centroid ← get_centroid(C);        error_obj ← DB_Loc_Sig_Error_Fit(centroid, C,loc_hyp.loc_sig_cluster, “USE ALL LOC SIGS IN                          DB”);                /* The above functioncall computes an error object, “error_obj”, providing a               measure of how similar the location signature cluster for“loc_hyp” is with the                verified location signatureclusters in the location signature data base,                wherein theverified location signature clusters are in the area represented by               the cell, C. See APPENDIX C for details of this function.*/         total_per_unit_error ← total_per_unit_error +[error_obj.error * error_obj.confidence / sizeof(C)];                /*The above statement computes an “error per unit of cell area” term as:                [error_obj.error * error_obj.confidence / sizeof(C)],wherein the error is the                 term: error_obj.error *error_obj.confidence. Subsequently, this error per                 unitof cell area term accumulated in “total_relative_error” */     }    avg_per_unit_error <-- total_per_unit_error / nbr_cells_in(mesh);        /* Now get a tunable constant, “tunable_constant”, that has beendetermined by the Adaptation         Engine 1382 (shown in Figs. 5, 6and 8), wherein “tunable_constant” may have been adapted to        environmental characteristics. */     tunable_constant ←get_tuneable_constant_for(“Historical_Location_Reasoner”, loc_hyp);        /* Now decrement the confidence value of “loc_hyp” by an erroramount that is scaled by         “tunable_constant” */    loc_hyp.confidence ← loc_hyp.confidence − [avg_per_unit_error *sizeof(covering) * tunable_constant];     RETURN(loc_hyp); }ENDOFHistorical_data_confidence_adjuster

What is claimed is:
 1. A method for locating a mobile unit (M),supported on a surface of the Earth, using wireless data obtained fortransmissions between the mobile unit M and one or more of a pluralityof terrestrial wireless communication stations, comprising: receivinglocation indicative wireless data obtained from one or more wirelesstransmissions of two way wireless communications between the mobile unitM, and one or more of the communication stations; wherein the locationindicative wireless data is for a plurality of wireless signalscommunicated to or from the mobile unit M, each signal, S, of theplurality of wireless signals: (i) has a different location from whichthe signal S is transmitted to, or received from, the mobile unit M, and(ii) for a portion of the location indicative wireless data for thesignal S, the portion is dependent upon a geographic position of thedifferent location for the signal S; obtaining a first location estimatefor the mobile unit M from a first location technique, wherein the firstlocation technique determines said first location estimate; wherein whenthe first location technique is supplied with input data obtained fromat least one wireless signal transmission between the mobile unit M andone of the communication stations (CS₁), wherein by the transmission,the mobile unit M detects the communication station CS₁, then the firstlocation technique uses an area data representation for locating themobile unit M, wherein the area data representation is indicative of awireless geographical area within which the communication station CS₁ isexpected to be detected by the mobile unit M; wherein the area datarepresentation is obtained from an analysis indicative of wirelesssignals being transmitted by the communication station CS₁ and beingdetected at geographical locations effective for determining thewireless geographical area, the analysis performed prior to the firstlocation technique being supplied with the input data; wherein theobtaining the first location estimate determines the first locationestimate relative to a known stationary terrestrial location where theone communication station CS₁ is located when the wireless geographicalarea is determined; obtaining a second location estimate for the mobileunit M using a second location technique, wherein the second locationtechnique determines said second location estimate using one or moreoccurrences of data obtained using the location indicative wirelessdata, wherein each of the occurrences is output by one or more patternrecognizers for estimating a location of one or more of the mobile unitM, wherein said second location estimate for the mobile unit M isdependent upon the pattern recognizers using data indicative of one ormore patterns in multipath for wireless signals communicated between:(a) one or more of the communication stations, and (b) said mobile unitM; determining a resulting location estimate of the mobile unit M,wherein at least one of the following (i) and (ii) is performed: (i)determining which of said first and second location estimates ispreferred for determining said resulting location estimate, and (ii)combining said first and second location estimates to determine saidresulting location estimate; wherein the resulting location estimateidentifies a likely location of the mobile unit M relative to one ormore fixed positions on the Earth; and outputting said resultinglocation estimate of the mobile unit M.
 2. The method of claim 1,wherein the obtaining the first location estimate includes providing atleast one value V, related to locating the mobile unit M, to a siteremote from the mobile unit M to determine said first location estimate.3. The method of claim 1, wherein at least one of: (1) the firstlocation technique, (2) the second location technique, and (3) the stepof determining a resulting location estimate, includes determining alikely geographical location for a location of the mobile unit M,wherein one or more of (d1)-(d3) following hold: (d1) the determining alikely geographical location is dependent upon multipath data obtainedfrom wireless signal multipath indicative information communicatedbetween the mobile unit M and the communication stations, (d2) thedetermining a likely geographical location is dependent upon (i) and(ii) following: (i) a representation of each of a plurality ofgeographical locations, and (ii) for each of the geographical locations,corresponding multipath indicative information previously obtained usingtransmissions between one or more mobile units and the communicationstations when the one or more mobile units transmitted at some earliertime, and (d3) the determining a likely geographical location includesselecting one or more of the geographical location representations thatare likely to be approximate to, or include the location of the mobileunit M.
 4. The method of claim 1, wherein at least one of the obtainingthe first location estimate and obtain the second location estimate isperformed at a site not co-located with the mobile unit M.
 5. The methodof claim 1, wherein the outputting includes transmitting said resultinglocation estimate via one of: a public telephone network and theInternet.
 6. The method of claim 1, wherein at least one of said firstand second location estimates is not dependent upon a value that isdependent upon a location of the mobile unit M, wherein the other ofsaid first and second location estimates is dependent upon the value. 7.The method of claim 1, wherein: (i) said first location estimate issubstantially dependent upon a first value for wireless signalscommunicated between the mobile unit M, and at least one of thecommunication stations, the first value dependent upon a location of themobile unit M, and (ii) said second location estimate is dependent upona second value for wireless signals communicated between the mobile unitM, and at least one of the communication stations, the second valuedependent upon the location of the mobile unit M; wherein said firstlocation estimate is not dependent on said second value, and said secondlocation estimate is not dependent on said first location estimate. 8.The method of claim 1, further including receiving, via a communicationsnetwork, a signal by a user of the mobile unit M for determining theresulting location estimate.
 9. The method of claim 1, wherein saidwireless transmissions include two way communication of voice databetween the mobile unit M and the one or more communication stations.10. The method of claim 1, wherein said mobile unit M is a hand-helddevice for wirelessly communicating via a telephony network: wherein thepattern recognizers determine a similarity, or an extent of similarity,between the patterns in multipath indicative of one or more wirelesssignal effects of one or more objects along a wireless transmission pathof wireless signals communicated between: (i) the mobile unit M, and(ii) one or more of the communication stations; and wherein the objectsaffect the wireless signals prior to being received by one of: (i) themobile unit M, and (ii) one or more of the communication stations. 11.The method of claim 1, wherein the two way wireless transmissions passthrough another mobile unit for communicating with the communicationstations, wherein the communication stations are stationary relative tothe Earth.
 12. The method of claim 1, wherein the determining theresulting location estimate includes receiving, for the mobile unit M,geographical location related information, the geographical locationrelated information received from a vehicle for locating the mobile unitM, wherein said geographical location related information is obtainedusing one or more of a direction of transmission and signal strengthfrom wireless transmissions by the mobile unit M received by thevehicle.
 13. The method of claim 1, wherein the receiving includesreceiving the location indicative wireless data via a transmission onthe Internet via an Internet communications protocol.
 14. The method ofclaim 1, wherein said resulting location estimate includes anidentification of a roadway in identifying the likely location of themobile unit M.
 15. The method of claim 1, wherein for locating themobile unit M at at least one geographical location, one of the firstand second obtaining is not performed.
 16. The method of claim 1,wherein the combining is performed, and the combining includes derivingsaid resulting location estimate as more dependent upon one of saidfirst and second location estimates deemed to be a better estimate ofthe location of the mobile unit M.
 17. A mobile unit location system forlocating a plurality of mobile units, each supported on a surface of theEarth, comprising machine components as follows: a network interface forreceiving one or more requests from a communications network, whereineach request is for locating a corresponding mobile unit of theplurality of mobile units, wherein during an instance of locating thecorresponding mobile unit, there is two way wireless communicationavailable between the corresponding mobile unit and at least one of aplurality of terrestrial communication stations; a location estimatorselector for communicating with a corresponding one or more of aplurality of location estimators; wherein for each of the requests, thelocation estimator selector selects each location estimator of therequest's corresponding one or more location estimators, according to anavailability of a corresponding input for the location estimator, forperforming the location estimator; wherein the corresponding input:corresponds to the request, is effective for the location estimator todetermine a location estimate of the corresponding mobile unit for therequest, and is dependent upon data obtained from a wireless signalcommunication between the corresponding mobile unit for the request andat least one of the communication stations; wherein the locationestimator selector is operably configured for communicating with each ofthe plurality of location estimators; wherein for at least one mobileunit, M, being the corresponding mobile unit for one of the requests (R)for locating the mobile unit M, the location estimator selectorcommunicates with the corresponding one or more of the locationestimators, for the request R, resulting in first and second of thelocation estimates for the request R, each of said first and secondlocation estimates having one or more of a geographical extent and alocation of the mobile unit M; wherein when one of the correspondinglocation estimators is supplied with its corresponding inputcorresponding to the request R, the first location estimate of themobile unit M is obtained from the one location estimator, wherein theone location estimator determines the first location estimate using datarepresenting a wireless geographical area for one of the communicationstations (CS₁); wherein the data representing the wireless geographicalarea is representative of a geographical extent within which the mobileunit M likely detects wireless transmissions from the communicationstation CS₁ and when the mobile unit M is not within the geographicalextent, the mobile unit M is not expected to be able to detect wirelesstransmissions from the communication station CS₁; wherein the datarepresenting the wireless geographical area is obtained from an analysisindicative of wireless signals being transmitted by the communicationstation CS₁ and being detected at geographical locations effective fordetermining the wireless geographical area, the analysis performed priorto the request R being received by the network interface; wherein thesecond location estimate of the mobile unit M is dependent upon amachine recognition of a pattern for wireless signals communicatedbetween: (a) one or more of the communication stations, and (b) themobile unit M; wherein the recognition of the pattern determines asimilarity, or an extent of similarity, between wireless signal dataindicative of one or more wireless signal effects of one or more objectsalong a wireless transmission path of a wireless signal forcommunication between: (i) the mobile unit M, and (ii) one of thecommunication stations; wherein the objects affect the wireless signalprior to being received by one of: (i) the mobile unit M, and (ii) theone communication station; a location determiner for determiningresulting location estimate data of the mobile unit M, wherein thelocation determiner receives the first and second location estimates andperforms at least one of (i) and (ii) following: (i) determinespreference information indicative of which of said first and secondlocation estimates is a more likely location of the mobile unit M, thepreference information for preferring one of the first and secondlocation estimates more than another of the first and second locationestimates for determining the resulting location estimate data, and (ii)combines said first and second location estimates for determining saidresulting location estimate data; wherein the resulting locationestimate data identifies a location of the mobile unit M; and an outputnetwork interface for transmitting the resulting location estimate datato a corresponding destination, wherein the corresponding destinationalso corresponds to one of the requests for locating the mobile unit M.18. The mobile unit location system of claim 17, wherein the outputnetwork interface transmits a map to the corresponding destination forpresenting, on the map, the location of the mobile unit M identified bythe resulting location estimate data.
 19. The mobile unit locationsystem of claim 17, wherein the location estimator selector communicateswith at least one of the location estimators via a network communicationbetween different network sites.
 20. A method for locating a pluralityof mobile units, each supported on a surface of the Earth, comprisingperforming the following by computational equipment: receiving, by anetwork interface, one or more requests from a communications network,wherein each request is for locating a corresponding mobile unit of theplurality of mobile units, wherein during an instance of locating thecorresponding mobile unit, there is two way wireless communicationavailable between the corresponding mobile unit and at least one of aplurality of terrestrial communication stations; selecting acorresponding one or more of a plurality of location estimators for eachof the requests; wherein for each of the requests, the selecting selectseach location estimator of the request's corresponding one or morelocation estimators, according to an availability of a correspondinginput for the location estimator, for performing the location estimator;wherein the corresponding input: corresponds to the request, iseffective for the location estimator to determine a location estimate ofthe corresponding mobile unit for the request, and is dependent upondata obtained from a wireless signal communication between thecorresponding mobile unit for the request and at least one of thecommunication stations; wherein for at least one of the correspondingmobile units (M), and for one of the requests (R) for locating themobile unit M, communicating with the corresponding one or more of thelocation estimators, for the request R, resulting in first and second ofthe location estimates for the request R, each of said first and secondlocation estimates having one or more of a geographical extent and alocation of the mobile unit M; wherein when one of the correspondinglocation estimators is supplied with its corresponding inputcorresponding to the request R, the first location estimate of themobile unit M is obtained from the one corresponding location estimator,wherein the one corresponding location estimator determines the firstlocation estimate using data representing a wireless geographical areafor one of the communication stations (CS₁); wherein the wirelessgeographical area is a geographical extent within which the mobile unitM is likely to be able to detect wireless transmissions from thecommunication station CS₁ and when the mobile unit M is not within thegeographical extent, the mobile unit M is not expected to be able todetect wireless transmissions from the communication station CS₁;wherein the data representing the wireless geographical area is obtainedfrom an analysis indicative of wireless signals being transmitted by thecommunication station CS₁ and being detected at geographical locationseffective for determining the wireless geographical area, the analysisperformed prior to the request R being received by the networkinterface; wherein the second location estimate of the mobile unit M isdependent upon a machine recognition of a pattern for wireless signalscommunicated between: (a) one or more of the communication stations, and(b) the mobile unit M; wherein the recognition of the pattern determinesa similarity, or an extent of similarity, between wireless signal dataindicative of one or more wireless signal effects of one or more objectsalong a wireless transmission path of a wireless signal forcommunication between: (i) the mobile unit M, and (ii) one or more ofthe communication stations; wherein the objects affect the wirelesssignal prior to being received by one of: (i) the mobile unit M, and(ii) one or more of the communication stations; and determiningresulting location estimate data of the mobile unit M using the firstand second location estimates, wherein at least one of (i) and (ii)following is performed: (i) determining preference informationindicative of which of said first and second location estimates is amore likely location of the mobile unit M, the preference informationfor preferring one of the first and second location estimates more thananother of the first and second location estimates for determining theresulting location estimate data, and (ii) combining said first andsecond location estimates for determining said resulting locationestimate data; wherein the resulting location estimate data identifies alocation of the mobile unit M; and transmitting on a network theresulting location estimate data to a corresponding destination, whereinthe corresponding destination also corresponds to one of the requestsfor locating the mobile unit M.
 21. The method of claim 20, whereinresulting location estimate data is dependent upon a measurement, T, ofa transition between a first spatial position of the mobile unit M and aprevious spatial position of the mobile unit M, wherein the measurementT is dependent upon an output from a mobile unit M resident devicecomponent that measures a characteristic of mobile unit M movement. 22.The method of claim 20, further including determining another locationestimate of the mobile unit M from one of the corresponding locationestimators, wherein the another location estimate is determined usingsignal related data obtained from a wireless signal transmissionreceived by the mobile unit M, the wireless signal transmission from oneof the communication stations not supported on the Earth's surface, thesignal related data providing signal timing information.
 23. The methodof claim 1, wherein the first location estimate and the second locationestimate are for a same location of the mobile unit M.
 24. The method ofclaim 1, wherein the first location estimate and the second locationestimate are for different locations of the mobile unit M.
 25. Themethod of claim 17, wherein the first location estimate and the secondlocation estimate are for a same location of the mobile unit M.
 26. Themethod of claim 17, wherein the first location estimate and the secondlocation estimate are for different locations of the mobile unit M. 27.The method of claim 20, wherein the first location estimate and thesecond location estimate are for a same location of the mobile unit M.28. The method of claim 20, wherein the first location estimate and thesecond location estimate are for different locations of the mobile unitM.